Simulation Argument Details for
simglm
2024-05-08
Source:vignettes/simulation_arguments.Rmd
simulation_arguments.RmdThe tidy simulation framework uses simulation arguments as the basis
for specifying the models to be simulated. The goal of this vignette is
to document more thoroughly the various arguments that are possible
within each function. It is recommended that new users start with the
“Tidy Simulation with simglm” vignette prior to working
through this vignette.
Fixed Arguments
Arguments associated with the fixed portion of the model are needed for each fixed variable that needs to be simulated. The fixed variables specified come from the formula argument. Interactions or the intercept are not included in the fixed arguments as these are generated automatically. Let’s start with an example.
library(simglm)
set.seed(321)
# To-DO: Add knot variable and debug
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
sample_size = list(level1 = 10, level2 = 20)
)
fixed_data <- simulate_fixed(data = NULL, sim_arguments)
head(fixed_data, n = 20)## X.Intercept. time weight age treat_1 treat level1_id id
## 1 1 0 231.1471 39 1 Control 1 1
## 2 1 1 158.6388 39 1 Control 2 1
## 3 1 2 171.6605 39 1 Control 3 1
## 4 1 3 176.4105 39 1 Control 4 1
## 5 1 4 176.2812 39 1 Control 5 1
## 6 1 5 188.0455 39 1 Control 6 1
## 7 1 6 201.8052 39 1 Control 7 1
## 8 1 7 186.9941 39 1 Control 8 1
## 9 1 8 190.1734 39 1 Control 9 1
## 10 1 9 163.4426 39 1 Control 10 1
## 11 1 0 190.4310 30 1 Control 1 2
## 12 1 1 224.5378 30 1 Control 2 2
## 13 1 2 185.6498 30 1 Control 3 2
## 14 1 3 253.2978 30 1 Control 4 2
## 15 1 4 145.3968 30 1 Control 5 2
## 16 1 5 155.8598 30 1 Control 6 2
## 17 1 6 193.6821 30 1 Control 7 2
## 18 1 7 192.6100 30 1 Control 8 2
## 19 1 8 197.3275 30 1 Control 9 2
## 20 1 9 193.3907 30 1 Control 10 2The following example shows the five types of variables that can be
generated. These types are specified with the var_type
argument and can be one of the following five types:
- ‘time’: This type of variable is used for a time metric variable
- ‘continuous’: This is a continuous distribution, default is to use
the
rnormdistribution function for generation. - ‘ordinal’: This generates ordinal data and is generated using the
samplefunction. - ‘factor’: This is a categorical or factor variable specification representing groups or categories.
- ‘knot’: This type of variable is generated based on another variable and represents a knot category variable.
Each variable type will be explored in more detail.
Finally, another common argument for all fixed variable types is the
argument var_level. This defaults to
var_level = 1 which would be a variable defined at the
level 1 of the model (i.e. unique value for each row in the data). These
can be changed to reflect the data level that is desired. For example,
var_level = 2 would repeat values for each level 2 cluster
found in the data and var_level = 3 would do the same for
each level 3 cluster. Therefore for variables that are at level 2 or
level 3, there will be fewer unique values in the data compared to level
1 variables.
Time Variable
For time variables used in longitudinal or repeated measures designs,
the default metric is 0 to level1 sample size minus 1. This can be seen
above in the output. To change the time variable metric the
time_levels argument can be used. A vector of values to
specify for the time variable can be given directly. For example, the
following could be passed to alter the metric of the time variable:
time_levels = c(0, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6) where
now the time variable would increment in 0.5 units for the first 7
measurements and 1 unit increments for the last three measurements. This
could represent a case when the measurements are collected every 6
months for the first 7 measurements and yearly after that.
Below is the output including the manual time variable specification.
The only requirement is that the length of the time_levels
argument must match the level1 sample size.
set.seed(321)
# To-DO: Add knot variable and debug
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time',
time_levels = c(0, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6)),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
sample_size = list(level1 = 10, level2 = 20)
)
fixed_data <- simulate_fixed(data = NULL, sim_arguments)
head(fixed_data, n = 20)## X.Intercept. time weight age treat_1 treat level1_id id
## 1 1 0.0 231.1471 39 1 Control 1 1
## 2 1 0.5 158.6388 39 1 Control 2 1
## 3 1 1.0 171.6605 39 1 Control 3 1
## 4 1 1.5 176.4105 39 1 Control 4 1
## 5 1 2.0 176.2812 39 1 Control 5 1
## 6 1 2.5 188.0455 39 1 Control 6 1
## 7 1 3.0 201.8052 39 1 Control 7 1
## 8 1 4.0 186.9941 39 1 Control 8 1
## 9 1 5.0 190.1734 39 1 Control 9 1
## 10 1 6.0 163.4426 39 1 Control 10 1
## 11 1 0.0 190.4310 30 1 Control 1 2
## 12 1 0.5 224.5378 30 1 Control 2 2
## 13 1 1.0 185.6498 30 1 Control 3 2
## 14 1 1.5 253.2978 30 1 Control 4 2
## 15 1 2.0 145.3968 30 1 Control 5 2
## 16 1 2.5 155.8598 30 1 Control 6 2
## 17 1 3.0 193.6821 30 1 Control 7 2
## 18 1 4.0 192.6100 30 1 Control 8 2
## 19 1 5.0 197.3275 30 1 Control 9 2
## 20 1 6.0 193.3907 30 1 Control 10 2Continuous Variable
Continuous variables are generating using distribution functions
(i.e. rnorm). Any distribution function found within R, or
user written distribution functions can be used, however the default
used in rnorm. To change the distribution function used,
the argument dist can be specified. For example, if the
Gamma distribution is desired for the weight variable, the following
code would achieve this:
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time',
time_levels = c(0, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6)),
weight = list(var_type = 'continuous', dist = 'rgamma',
shape = 3),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
sample_size = list(level1 = 10, level2 = 20)
)
fixed_data <- simulate_fixed(data = NULL, sim_arguments)
head(fixed_data, n = 20)## X.Intercept. time weight age treat_1 treat level1_id id
## 1 1 0.0 5.922362 41 0 Treatment 1 1
## 2 1 0.5 1.500918 41 0 Treatment 2 1
## 3 1 1.0 1.894797 41 0 Treatment 3 1
## 4 1 1.5 2.307843 41 0 Treatment 4 1
## 5 1 2.0 1.975996 41 0 Treatment 5 1
## 6 1 2.5 2.882207 41 0 Treatment 6 1
## 7 1 3.0 3.064936 41 0 Treatment 7 1
## 8 1 4.0 1.703499 41 0 Treatment 8 1
## 9 1 5.0 7.518809 41 0 Treatment 9 1
## 10 1 6.0 2.442868 41 0 Treatment 10 1
## 11 1 0.0 7.855513 52 1 Control 1 2
## 12 1 0.5 1.008858 52 1 Control 2 2
## 13 1 1.0 3.452912 52 1 Control 3 2
## 14 1 1.5 7.881209 52 1 Control 4 2
## 15 1 2.0 2.262056 52 1 Control 5 2
## 16 1 2.5 3.255559 52 1 Control 6 2
## 17 1 3.0 4.160648 52 1 Control 7 2
## 18 1 4.0 2.333586 52 1 Control 8 2
## 19 1 5.0 1.900442 52 1 Control 9 2
## 20 1 6.0 1.445119 52 1 Control 10 2This would be the resulting distribution of the weight variable generated.
Floor or Ceiling Effects
It is also possible to simulate floor or ceiling effects for continuous attributes. This may be of interest in a situation when data are simulated that would be distributed a specific way, but through the measurement process, the scores are truncated by a floor or ceiling value. One common example for the presence of a floor effect is for age, which would start at 0. Ceiling effects could be possible for very easy exams where many individuals may achieve a perfect score, so the ceiling effect would impose this constraint.
To impose floor or ceiling effects, there are optional arguments when
specifying a continuous attribute named, floor and
ceiling respectively that would impose floor and/or ceiling
constraints. It is possible to use both for the same attribute as well.
Below is an example of specifying these for two continuous attributes.
In the following example,
sim_arguments <- list(
formula = y ~ 1 + age + exam,
fixed = list(age = list(var_type = 'continuous',
mean = 6, sd = 1,
floor = 5),
exam = list(var_type = 'continuous',
mean = 90, sd = 6,
ceiling = 100)),
sample_size = 50
)
sim_data <- simulate_fixed(data = NULL, sim_arguments)
summary(sim_data$age)
summary(sim_data$exam)Ordinal Variable
Ordinal variable specification uses the sample function
within R to generate ordinal variables that are whole integers. The
required argument for these types of variables is levels.
The levels argument takes a range or vector of integer
values to be passed to the sample function within R. For
example, these three specifications for the levels argument
are valid: 3:60, seq(4, 60, 2),
c(3, 10, 18, 24, 54, 60).
An additional optional argument is replace. The
replace argument specified whether the sampling is done
with or without replacement. The default behavior is to do sampling with
replacement. If sampling without replacement is desired set
replace = FALSE. See sample for more details
on this argument.
Finally, the probability of selecting each value specified to the
levels argument is also able to be specified through the
prob argument. If prob is specified, it takes
a vector of probabilities that must be the same length as the levels
argument. The default behavior is for each value specified with
levels to be equally likely to be sampled.
Factor Variable
Factor variables are generated similarly to ordinal variables with
the sample function, however factor variables allow the
generation of text or categorical variables in addition to numeric
grouping variables. Therefore the only needed argument for factor
variables is a vector of numeric or text strings representing the
different groups to be generated. For example, both of these
specifications are equivalent: c(1, 2, 3, 4) and
c('Freshman', 'Sophomore', 'Junior', 'Senior'). Both of
these specifications would generate data for these four groups, the
difference is that the text labels will be used when text strings are
specified.
An additional optional argument is replace. The
replace argument specified whether the sampling is done
with or without replacement. The default behavior is to do sampling with
replacement. If sampling without replacement is desired set
replace = FALSE. See sample for more details
on this argument.
The probability of selecting each value specified to the
levels argument is also able to be specified through the
prob argument. If prob is specified, it takes
a vector of probabilities that must be the same length as the levels
argument. The default behavior is for each value specified with
levels to be equally likely to be sampled.
Force Balanced Simulation
It is also possible to simulate data from a balanced factorial design
by including the force_equal = TRUE argument. This argument
will cause the simulate_fixed() function to produce a data
set with an equal number of observations for each level of the factor
variable, instead of randomly sampling from the levels. To see the
difference, compare the difference between the two following
simulations:
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + weight + treat ,
fixed = list(weight = list(var_type = 'continuous', dist = 'rgamma',
shape = 3
),
treat = list(var_type = 'factor',
levels = c('Treatment A', 'Treatment B', 'Control')
)
),
sample_size = 33
)
unbalanced_data <- simulate_fixed(data = NULL, sim_arguments)
dplyr::count(unbalanced_data, treat) # unequal number of control and treatment observations## treat n
## 1 Treatment A 11
## 2 Treatment B 6
## 3 Control 16
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + weight + treat ,
fixed = list(weight = list(var_type = 'continuous', dist = 'rgamma',
shape = 3
),
treat = list(var_type = 'factor',
levels = c('Treatment A', 'Treatment B', 'Control'),
force_equal = TRUE
)
),
sample_size = 33
)
balanced_data <- simulate_fixed(data = NULL, sim_arguments)
dplyr::count(balanced_data, treat) # equal number of control and treatment observations## treat n
## 1 Treatment A 11
## 2 Treatment B 11
## 3 Control 11Note that the force_equal = TRUE argument is
incompatible with the replace = FALSE option. The
force_equal = TRUE argument supersedes the
prob argument - in other words, specifying
force_equal = TRUE as well as specifying specific sampling
probabilities will not cause an error, but doesn’t make conceptual
sense, and those sampling probabilities will not be reflected in the
simulated data.
It is also possible to specify force_equal = TRUE when
groups are not explicitly equal. In this situation it will get as close
to equal as it possibly can, usually within one or two observations of
each other. Note, that this will not explicitly retain group size across
replications, but they will be within one or two across replications.
This behavior would likely be similar across replications than the
default approach that would imply the probabilities being equal in the
long run.
The following code adjusts the sample size to be 35, which is not equally dividable by 3, the group size. As such, the resulting count of the number of observations within each group is close to being equal.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + weight + treat ,
fixed = list(weight = list(var_type = 'continuous', dist = 'rgamma',
shape = 3
),
treat = list(var_type = 'factor',
levels = c('Treatment A', 'Treatment B', 'Control'),
force_equal = TRUE
)
),
sample_size = 35
)
balanced_data <- simulate_fixed(data = NULL, sim_arguments)
dplyr::count(balanced_data, treat) # equal number of control and treatment observations## treat n
## 1 Treatment A 13
## 2 Treatment B 11
## 3 Control 11Knot Variables
Knot variables are defined as those that are a prominent point based on a specific spot of another variable. A common example of these types of variables would occur in interrupted time series data, where there is a place in time where the treatment is given after a series of baselines. The place where the treatment is given is the knot location.
In simglm, knots are specified within the formula, it is made
particularly explicit below with the name: age_knot. Adding
_knot at the end of a knot variable is not needed, but is
used here to be more illustrative. When specifying knot attributes, the
specific behavior of these attributes are controlled through the
knot named element of the simulation arguments. See the
example below with the simulation arguments for the variable,
age_knot.
sim_args <- list(
formula = y ~ 1 + age + age_knot,
fixed = list(age = list(var_type = 'ordinal', levels = 30:60)),
knot = list(age_knot = list(variable = 'age',
knot_locations = 50)),
sample_size = 500,
error = list(variance = 10),
reg_weights = c(2, .5, 1.5)
)The required elements to simulate a knot variable is the variable to
base the knot location on. This is specified with,
variable = 'age' above. The second required element is the
argument, knot_locations which specifies the location where
the knot is placed. The knot variable itself is a variable that takes on
the number of knot locations plus one, therefore in this example, the
age_knot variable takes on two values. This would be
similar to an indicator or dummy variable used in a linear regression
model.
Similar to above, the simulate_fixed() function does the
simulation of this fixed effect term.
simulate_fixed(data = NULL, sim_args = sim_args) |>
head()## X.Intercept. age age_knot level1_id
## 1 1 56 1 1
## 2 1 30 0 2
## 3 1 48 0 3
## 4 1 38 0 4
## 5 1 53 1 5
## 6 1 60 1 6As specified, this term would represent a change in the intercept for
those that are larger than the knot location, above this was specified
as 50 years. More complicated structures can be obtained. One example
shown here is that an interaction between the knot variable and the age
variable can be made. This term would then represent a change in slope
for those that are older than 50. The only change needed is to add this
term to the specification within the formula and also add another term
to the reg_weights argument to specify on average the
magnitude of the slope change.
sim_args <- list(
formula = y ~ 1 + age + age_knot + age:age_knot,
fixed = list(age = list(var_type = 'ordinal', levels = 30:60)),
knot = list(age_knot = list(variable = 'age',
knot_locations = 50)),
sample_size = 500,
error = list(variance = 1000),
reg_weights = c(2, .5, 1.5, 10)
)
simulate_fixed(data = NULL, sim_args = sim_args) |>
head()## X.Intercept. age age_knot age.age_knot level1_id
## 1 1 30 0 0 1
## 2 1 38 0 0 2
## 3 1 33 0 0 3
## 4 1 42 0 0 4
## 5 1 52 1 52 5
## 6 1 40 0 0 6Random Error Arguments
By default, the random error is generated as random normal with a mean of 0 and standard deviation of 1. If this is the desired behavior, no additional simulation arguments need to be specified in the simulation arguments. For example, the code below generates random error using the fixed arguments already shown above.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
sample_size = list(level1 = 10, level2 = 20)
)
error_data <- simulate_error(data = NULL, sim_arguments)
head(error_data, n = 20)## error level1_id id
## 1 1.7049032 1 1
## 2 -0.7120386 2 1
## 3 -0.2779849 3 1
## 4 -0.1196490 4 1
## 5 -0.1239606 5 1
## 6 0.2681838 6 1
## 7 0.7268415 7 1
## 8 0.2331354 8 1
## 9 0.3391139 9 1
## 10 -0.5519147 10 1
## 11 0.3477014 1 2
## 12 1.4845918 2 2
## 13 0.1883255 3 2
## 14 2.4432598 4 2
## 15 -1.1534395 5 2
## 16 -0.8046717 6 2
## 17 0.4560691 7 2
## 18 0.4203326 8 2
## 19 0.5775845 9 2
## 20 0.4463561 10 2Optional Arguments for Random Error
The two main optional arguments for the random error component
include a dist and variance that represent the
error generating function and variance of the random error respectively.
For example, if a t-distribution is desired for simulation of random
error, dist = 'rt' can be used to specify the
t-distribution as the generating distribution. When distributions other
than random normal are specified, it is common that additional arguments
will need to be specified for the generating distribution function
(i.e. rt). In the rt example, the
df argument is needed to specify the degrees of freedom for
the t-distribution. Below is an example using a t-distribution.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
error = list(dist = 'rt', df = 4),
sample_size = list(level1 = 10, level2 = 20)
)
error_data <- simulate_error(data = NULL, sim_arguments)
head(error_data, n = 20)## error level1_id id
## 1 2.7754417 1 1
## 2 -0.6105358 2 1
## 3 0.2388096 3 1
## 4 0.2364648 4 1
## 5 -0.5580779 5 1
## 6 1.5918703 6 1
## 7 5.3320327 7 1
## 8 0.3197329 8 1
## 9 -0.1541429 9 1
## 10 -1.7929619 10 1
## 11 -1.4292669 1 2
## 12 -1.6045961 2 2
## 13 -1.0007634 3 2
## 14 -1.5413444 4 2
## 15 -0.1818689 5 2
## 16 -1.7195087 6 2
## 17 0.2629315 7 2
## 18 0.8104919 8 2
## 19 0.2150117 9 2
## 20 2.1937363 10 2Finally, the variance of the random error can also be specified to be
something other than the default 1. For example, if the variance is
desired to be 10, then the argument variance = 10 will set
this variance. This variance works with any distribution function. For
example, the following will generate data as a t-distribution with a
variance of 10.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
error = list(dist = 'rt', df = 4, variance = 10),
sample_size = list(level1 = 10, level2 = 20)
)
error_data <- simulate_error(data = NULL, sim_arguments)
var(error_data$error)## [1] 19.36487Note that the variance does not actually equal 10 here. This is due
to the generating distribution specified (i.e. t-distribution with 4
degrees of freedom) has a theoretical variance of 2. In cases when the
random error has a variance other than 1, the variable needs to be
standardized prior to converting to the desired variance value. The
standardization can be done in two ways. One, an empirical mean and
standard deviation of the generating distribution can be obtained by
setting the argument ther_sim = TRUE. For example:
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
error = list(dist = 'rt', df = 4, variance = 10, ther_sim = TRUE),
sample_size = list(level1 = 10, level2 = 20)
)
error_data <- simulate_error(data = NULL, sim_arguments)
var(error_data$error)## [1] 6.893405This simulation takes longer to run as the generated empirical mean
and standard deviation for the standardization draws a large number of
random values from the specified distribution. To speed up this process,
the theoretical values can be specified directly within the
ther_val argument as a vector with the mean specified first
followed by the standard deviation. This is shown below:
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
error = list(dist = 'rt', df = 4, variance = 10, ther_val = c(0, sqrt(2))),
sample_size = list(level1 = 10, level2 = 20)
)
error_data <- simulate_error(data = NULL, sim_arguments)
var(error_data$error)## [1] 9.682433Heterogeneity of Variance
It is also possible to generate heterogeneity of variance for the random error.
simulation_arguments <- list(
formula = y ~ 1 + group,
fixed = list(group = list(var_type = 'factor',
levels = c('male', 'female'))),
sample_size = 500,
error = list(variance = 1),
heterogeneity = list(variable = 'group',
variance = c(1, 8)),
reg_weights = c(0, .15)
)
hetero_data <- simulate_fixed(data = NULL, simulation_arguments) |>
simulate_error(simulation_arguments) |>
simulate_heterogeneity(simulation_arguments)## # A tibble: 2 × 3
## group var_error var_o_error
## <fct> <dbl> <dbl>
## 1 male 1.08 1.08
## 2 female 8.25 1.03Random Effect Arguments
The random effect arguments are passed through a named element of the list to the simulation arguments called ‘randomeffect’. Within this element, each random effect specified in the formula must be specified as a named list. The named list allows the user to specify the names that will be used in the data simulation for the random effects. The default behavior of these random effects is to be simulated from a random normal distribution with mean 0 and variance of 1.
Many of the same arguments that have been discussed with the error
simulation are possible for the random effect simulation arguments.
These include: dist, variance,
ther_sim, ther_val and other arguments that
are needed to pass on the data generating function. In addition, the
argument var_level first introduced with the fixed effects
are present here indicating which level of the data structure the random
effect should be generated at. These arguments will not be discussed in
more detail here, but readers can see examples of these arguments within
the fixed effects and random error sections above.
Below is an example that simulated two random effects in a two-level nested model and specifies the variance of 8 and 3 for the two random effects respectively. Finally, you will notice in the final output, the names of the random effect columns are specified by the names included in the list associated with the “randomeffect” portion of the simulation arguments.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
time_id = list(variance = 3, var_level = 2)),
sample_size = list(level1 = 10, level2 = 20)
)
random_data <- simulate_randomeffect(data = NULL, sim_arguments)
head(random_data, n = 20)## int_id time_id level1_id id
## 1 4.822195 1.588733 1 1
## 2 4.822195 1.588733 2 1
## 3 4.822195 1.588733 3 1
## 4 4.822195 1.588733 4 1
## 5 4.822195 1.588733 5 1
## 6 4.822195 1.588733 6 1
## 7 4.822195 1.588733 7 1
## 8 4.822195 1.588733 8 1
## 9 4.822195 1.588733 9 1
## 10 4.822195 1.588733 10 1
## 11 -2.013949 -0.185436 1 2
## 12 -2.013949 -0.185436 2 2
## 13 -2.013949 -0.185436 3 2
## 14 -2.013949 -0.185436 4 2
## 15 -2.013949 -0.185436 5 2
## 16 -2.013949 -0.185436 6 2
## 17 -2.013949 -0.185436 7 2
## 18 -2.013949 -0.185436 8 2
## 19 -2.013949 -0.185436 9 2
## 20 -2.013949 -0.185436 10 2Multiple Membership Random Effects
If multiple membership random effects are desired, these can be
specified directly within the formula syntax as you would with lme4. For
example,
y ~ 1 + time + weight + age + treat + (1 + time| id) + (1 | neighborhood_id).
When documenting the simulation arguments for the additional multiple
membership random effect (i.e. (1 | neighborhood_id)),
specifying multiple_member = TRUE with that random effect
to identify that this is indeed a multiple membership random effect.
Secondly, you can also specify directly the number of clusters that are
associated with this multiple membership factor, this can be different
than those specified in the sample_size argument. This can
be done with the num_ids argument. Below is an example with
a single multiple membership random effect representing neighborhoods
individuals belong in
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id) +
(1 | neighborhood_id),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
time_id = list(variance = 3, var_level = 2),
int_nid = list(variance = 5, var_level = 2,
multiple_member = TRUE,
num_ids = 12)),
sample_size = list(level1 = 10, level2 = 20)
)
random_data <- simulate_randomeffect(data = NULL, sim_arguments)
head(random_data, n = 20)## int_id time_id int_nid neighborhood_id level1_id id
## 1 4.822195 1.588733 0.7319884 1 1 1
## 2 4.822195 1.588733 0.7319884 1 2 1
## 3 4.822195 1.588733 0.7319884 1 3 1
## 4 4.822195 1.588733 0.7319884 1 4 1
## 5 4.822195 1.588733 0.7319884 1 5 1
## 6 4.822195 1.588733 0.7319884 1 6 1
## 7 4.822195 1.588733 0.7319884 1 7 1
## 8 4.822195 1.588733 0.7319884 1 8 1
## 9 4.822195 1.588733 0.7319884 1 9 1
## 10 4.822195 1.588733 0.7319884 1 10 1
## 11 -2.013949 -0.185436 0.7319884 1 1 2
## 12 -2.013949 -0.185436 0.7319884 1 2 2
## 13 -2.013949 -0.185436 0.7319884 1 3 2
## 14 -2.013949 -0.185436 0.7319884 1 4 2
## 15 -2.013949 -0.185436 0.7319884 1 5 2
## 16 -2.013949 -0.185436 0.7319884 1 6 2
## 17 -2.013949 -0.185436 -5.0111674 2 7 2
## 18 -2.013949 -0.185436 -5.0111674 2 8 2
## 19 -2.013949 -0.185436 -5.0111674 2 9 2
## 20 -2.013949 -0.185436 -5.0111674 2 10 2Correlated Fixed and Random Effects
Data attributes are often correlated, therefore, in order to really
mimic real-world data, simulated data that are correlated would be
desired. This can be done with simglm by using the function
correlated_variables(). This function processes a new set
of simulation arguments specified with the named correlate.
In what follows, discussion of correlating fixed effects will be
explored first, followed by correlating random effects next.
Fixed Effect Correlation
sim_args <- list(formula = y ~ 1 + act + gpa + commute_time,
fixed = list(act = list(var_type = 'ordinal',
levels = 15:36),
gpa = list(var_type = 'continuous',
mean = 2,
sd = .5),
commute_time = list(var_type = 'continuous',
mean = 15,
sd = 6)),
correlate = list(fixed = data.frame(x = c('act', 'act', 'gpa'),
y = c('gpa', 'commute_time', 'commute_time'),
corr = c(0.5, .6, .2))),
sample_size = 1000)
correlate_attribute <- simulate_fixed(data = NULL, sim_args) |>
correlate_variables(sim_args)
head(correlate_attribute)## X.Intercept. act_old gpa_old commute_time_old level1_id act_corr gpa
## 1 1 31 2.542058 19.84992 1 33.34939 2.007960
## 2 1 30 1.094168 11.30912 2 24.89321 2.123880
## 3 1 31 1.824700 13.73815 3 29.60763 2.215003
## 4 1 16 1.573188 15.26606 4 14.56133 1.552374
## 5 1 20 1.344491 19.25067 5 17.05369 1.321968
## 6 1 16 1.389140 15.84091 6 13.60838 1.452813
## commute_time act
## 1 16.40195 33
## 2 24.17105 25
## 3 20.65193 30
## 4 10.05897 15
## 5 14.62946 17
## 6 11.14732 15
select(correlate_attribute, -X.Intercept., -level1_id) |>
cor()## act_old gpa_old commute_time_old act_corr gpa
## act_old 1.00000000 0.04962541 0.04835660 0.9188642 0.3863944
## gpa_old 0.04962541 1.00000000 -0.04152076 0.4396757 0.3779704
## commute_time_old 0.04835660 -0.04152076 1.00000000 0.0300287 -0.8447945
## act_corr 0.91886422 0.43967570 0.03002870 1.0000000 0.4942777
## gpa 0.38639439 0.37797036 -0.84479447 0.4942777 1.0000000
## commute_time 0.86267143 -0.46233028 0.06300074 0.5931195 0.1524556
## act 0.92275601 0.41973540 0.03151538 0.9956235 0.4881891
## commute_time act
## act_old 0.86267143 0.92275601
## gpa_old -0.46233028 0.41973540
## commute_time_old 0.06300074 0.03151538
## act_corr 0.59311946 0.99562355
## gpa 0.15245559 0.48818907
## commute_time 1.00000000 0.60667173
## act 0.60667173 1.00000000Random Effect Correlation
sim_args <- list(formula = y ~ 1 + act + gpa + sat + (1 + act | id),
fixed = list(act = list(var_type = 'continuous',
mean = 20,
sd = 4),
gpa = list(var_type = 'continuous',
mean = 2,
sd = .5),
sat = list(var_type = 'continuous',
mean = 500,
sd = 100)),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
act_id = list(variance = 3, var_level = 2)),
sample_size = list(level1 = 100, level2 = 5000),
correlate = list(random = data.frame(x = 'int_id', y = 'act_id',
corr = .3))
)
random_correlate <- simulate_randomeffect(data = NULL, sim_args) |>
correlate_variables(sim_args)
head(random_correlate)## int_id_old act_id_old level1_id id int_id act_id
## 1 -5.137806 -2.204438 1 1 4.540962 3.362763
## 2 -5.137806 -2.204438 2 1 4.540962 3.362763
## 3 -5.137806 -2.204438 3 1 4.540962 3.362763
## 4 -5.137806 -2.204438 4 1 4.540962 3.362763
## 5 -5.137806 -2.204438 5 1 4.540962 3.362763
## 6 -5.137806 -2.204438 6 1 4.540962 3.362763
select(random_correlate, -level1_id, -id) |>
cor()## int_id_old act_id_old int_id act_id
## int_id_old 1.000000000 0.001196824 -0.9888210 -0.4418334
## act_id_old 0.001196824 1.000000000 0.1479240 -0.8976253
## int_id -0.988820977 0.147924047 1.0000000 0.3031302
## act_id -0.441833443 -0.897625255 0.3031302 1.0000000Unbalanced level 2 and 3 designs
Often it is of interest to not have the same number of observations within each cluster when simulated data that are clustered. For example, imagine a situation within education where there are students nested in classrooms which are nested in schools. You could build this example further, but for this example, suppose this is the hierarchy that is of interest to model. In most situations, the number of classrooms and the number of students within those classrooms would not be the same. Instead, it is most likely that the number of classrooms would vary across schools and the number of students would vary across classrooms within the same school.
To be very explicit, one school may have 15 classrooms, another may have 35 classrooms, and another could have 100 classrooms. Within the school with 15 classrooms, the classrooms may average 20 students in each, but may vary between 15 to 25 students across all 15 classrooms. This is important to simulate to mimic real world data that would be collected.
Unbalanced Level 2 Example
Suppose only students and classrooms are focused on first. To
generate realistic situations, it is of interest to vary the number of
students within each classroom. To do this, the runif()
function can be used to generate random numbers between specified
minimum and maximum classroom sizes. A user can also specify the values
directly in a vector of samples sizes that are the same size as the
number of level 2 units (classrooms here).
## [1] 20 14 16 20 26 26 22 21 15 28 15 22 25 20 22The “level1_ss” vector are whole integers representing the sample size for each classroom. Below, these data are simulated. Notice that the “level1_ss” object is passed as an element of the level one sample size. Finally, a count of the number of observations in each classroom are shown, notice how these are the same as the values in the “level1_ss” object.
sim_arguments <- list(
formula = test_scores ~ 1 + gpa + (1 | classroom),
fixed = list(
gpa = list(var_type = 'continuous', mean = 3, sd = 0.5,
ceiling = 4, floor = 0)
),
random = list(
classroom_int = list(variance = 2, var_level = 2)
),
error = list(variance = 5),
reg_weights = c(50, 0.5),
sample_size = list(
level1 = level1_ss,
level2 = 15
)
)
simulate_fixed(data = NULL, sim_arguments) |>
dplyr::count(classroom)## classroom n
## 1 1 20
## 2 2 14
## 3 3 16
## 4 4 20
## 5 5 26
## 6 6 26
## 7 7 22
## 8 8 21
## 9 9 15
## 10 10 28
## 11 11 15
## 12 12 22
## 13 13 25
## 14 14 20
## 15 15 22Unbalanced Level 3 Example
This can be further specified to work with the full level 3 example discussed above with students nested within classrooms which are nested in schools. Suppose there are 25 schools that are included in the sample frame. Within these 25 schools, suppose there are between 8 and 50 classrooms.
## [1] 27 50 45 18 18 44 20 38 25 32 22 26 39 10 16 42 44 9 29 29 22 45 40 17 26Each number for the level 2 sample size object represents the number of classrooms that are in each of the 25 schools. For example, the first number above, 27, is the number of classrooms in the first school. Once the unbalanced level 2 sample size is specified, then, the level 1 sample size for each classroom can be generated. To generate the level 1 sample size, we need the sum of the level 2 sample sizes. That is, we need to generate a sample size for each of the 27 classrooms for the first school and so on. Therefore, a total of 733 sample sizes will need to be generated as that is the total number of classrooms simulated across all 25 schools. Suppose that each classroom ranges from 12 to 28 students.
## [1] 27 26 24 22 21 27The sample size for the first six classrooms are shown. These could now be specified within the simulation arguments to extend the code generated in the level 2 example.
sim_arguments <- list(
formula = test_scores ~ 1 + gpa + (1 | classroom) + (1 | school),
fixed = list(
gpa = list(var_type = 'continuous', mean = 3, sd = 0.5,
ceiling = 4, floor = 0)
),
randomeffect = list(
classroom_int = list(variance = 2, var_level = 2),
school_int = list(variance = 0.5, var_level = 3)
),
error = list(variance = 5),
reg_weights = c(50, 0.5),
sample_size = list(
level1 = level1_ss,
level2 = level2_ss,
level3 = 25
)
)
simulate_fixed(data = NULL, sim_arguments) |>
dplyr::count(school, classroom) |>
head()## school classroom n
## 1 1 1 27
## 2 1 2 26
## 3 1 3 24
## 4 1 4 22
## 5 1 5 21
## 6 1 6 27The sample sizes for the first 6 classrooms for the first school are shown. These values will match the level1_ss object shown above.
Missing Data Arguments
Missing data is the standard rather than the exception in real world data. Therefore, generating data that include missing is important for generating data that are representative of real world data. In the power analysis context, most power analyses do not include missing data in the power computations (particularly if these are closed form solutions), but power can be negatively affected by missing data.
Fortunatly, the simglm package contains many useful frameworks for
generating missing data. To generate missing data, the
generate_missing function can be used for this. This
function is called after the complete data are generated. This means
that the complete data and missing data will be generated. Currently
three types of missing data are supported, dropout, random, and mar
(missing at random). The specification of missing data arguments is done
using a named element to the simulation argument list called
“missing_data”.
Random Missing Data
First, lets explore random missing data. This structure is equivalent
to the missing completely at random framework if you are familiar with
Rubin’s missing data classifications. To generate random missing data
the type = 'random' is specified. Two additional arguments
are needed to generate the missing data, first the missing proportion
needs to be specified with miss_prop and the name of the
new response variable with missing data needs to be specified with
new_outcome. Below is an example generating data with
random missing data.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
reg_weights = c(4, 0.5, 0.75, 0, 0.33),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
time_id = list(variance = 3, var_level = 2)),
missing_data = list(miss_prop = .25, new_outcome = 'y_missing',
type = 'random'),
sample_size = list(level1 = 10, level2 = 20)
)
data_w_missing <- simulate_fixed(data = NULL, sim_arguments) |>
simulate_randomeffect(sim_arguments) |>
simulate_error(sim_arguments) |>
generate_response(sim_arguments) |>
generate_missing(sim_arguments)
head(data_w_missing, n = 10)## X.Intercept. time weight age treat_1 treat level1_id id int_id
## 1 1 0 231.1471 39 1 Control 1 1 0.2244243
## 2 1 1 158.6388 39 1 Control 2 1 0.2244243
## 3 1 2 171.6605 39 1 Control 3 1 0.2244243
## 4 1 3 176.4105 39 1 Control 4 1 0.2244243
## 5 1 4 176.2812 39 1 Control 5 1 0.2244243
## 6 1 5 188.0455 39 1 Control 6 1 0.2244243
## 7 1 6 201.8052 39 1 Control 7 1 0.2244243
## 8 1 7 186.9941 39 1 Control 8 1 0.2244243
## 9 1 8 190.1734 39 1 Control 9 1 0.2244243
## 10 1 9 163.4426 39 1 Control 10 1 0.2244243
## time_id error fixed_outcome random_effects y miss_prob
## 1 2.533554 2.382697049 177.6903 0.2244243 180.2974 0.646
## 2 2.533554 -0.319179378 123.8091 2.7579779 126.2479 0.088
## 3 2.533554 0.716756305 134.0753 5.2915315 140.0836 0.534
## 4 2.533554 -0.314477357 138.1379 7.8250850 145.6485 0.560
## 5 2.533554 -0.325146339 138.5409 10.3586386 148.5744 0.119
## 6 2.533554 0.006078237 147.8641 12.8921922 160.7624 0.415
## 7 2.533554 1.286803356 158.6839 15.4257457 175.3965 0.718
## 8 2.533554 -1.039387080 148.0755 17.9592993 164.9955 0.697
## 9 2.533554 0.385799112 150.9601 20.4928529 171.8387 0.928
## 10 2.533554 1.927576531 131.4119 23.0264064 156.3659 0.829
## miss_prob y_missing
## 1 0 180.2974
## 2 1 NA
## 3 0 140.0836
## 4 0 145.6485
## 5 1 NA
## 6 0 160.7624
## 7 0 175.3965
## 8 0 164.9955
## 9 0 171.8387
## 10 0 156.3659We can look at the amount of missing data:
prop.table(table(is.na(data_w_missing$y_missing)))##
## FALSE TRUE
## 0.735 0.265MAR Missing Data
Data that are missing at random (MAR) can be generated as well.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
reg_weights = c(4, 0.5, 0.75, 0, 0.33),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30,
var_level = 1),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
time_id = list(variance = 3, var_level = 2)),
missing_data = list(new_outcome = 'y_missing', miss_cov = 'weight',
mar_prop = seq(from = 0, to = .9, length.out = 200),
type = 'mar'),
sample_size = list(level1 = 10, level2 = 20)
)
data_w_missing <- simulate_fixed(data = NULL, sim_arguments) |>
simulate_randomeffect(sim_arguments) |>
simulate_error(sim_arguments) |>
generate_response(sim_arguments) |>
generate_missing(sim_arguments)
head(data_w_missing, n = 10)## X.Intercept. time weight age treat_1 treat level1_id id int_id
## 1 1 0 231.1471 39 1 Control 1 1 0.2244243
## 2 1 1 158.6388 39 1 Control 2 1 0.2244243
## 3 1 2 171.6605 39 1 Control 3 1 0.2244243
## 4 1 3 176.4105 39 1 Control 4 1 0.2244243
## 5 1 4 176.2812 39 1 Control 5 1 0.2244243
## 6 1 5 188.0455 39 1 Control 6 1 0.2244243
## 7 1 6 201.8052 39 1 Control 7 1 0.2244243
## 8 1 7 186.9941 39 1 Control 8 1 0.2244243
## 9 1 8 190.1734 39 1 Control 9 1 0.2244243
## 10 1 9 163.4426 39 1 Control 10 1 0.2244243
## time_id error fixed_outcome random_effects y miss_prop
## 1 2.533554 2.382697049 177.6903 0.2244243 180.2974 0.8683417
## 2 2.533554 -0.319179378 123.8091 2.7579779 126.2479 0.2035176
## 3 2.533554 0.716756305 134.0753 5.2915315 140.0836 0.3165829
## 4 2.533554 -0.314477357 138.1379 7.8250850 145.6485 0.3844221
## 5 2.533554 -0.325146339 138.5409 10.3586386 148.5744 0.3798995
## 6 2.533554 0.006078237 147.8641 12.8921922 160.7624 0.5562814
## 7 2.533554 1.286803356 158.6839 15.4257457 175.3965 0.7236181
## 8 2.533554 -1.039387080 148.0755 17.9592993 164.9955 0.5201005
## 9 2.533554 0.385799112 150.9601 20.4928529 171.8387 0.5743719
## 10 2.533554 1.927576531 131.4119 23.0264064 156.3659 0.2442211
## miss_prob missing y_missing
## 1 0.64591577 1 NA
## 2 0.08759992 1 NA
## 3 0.53359833 0 140.0836
## 4 0.55954188 0 145.6485
## 5 0.11853504 1 NA
## 6 0.41452566 1 NA
## 7 0.71808307 1 NA
## 8 0.69699814 0 164.9955
## 9 0.92751823 0 171.8387
## 10 0.82901588 0 156.3659We can look at the amount of missing data:
prop.table(table(is.na(data_w_missing$y_missing)))##
## FALSE TRUE
## 0.54 0.46Dropout Missing Data
Dropout missing data is possible when simulating data from a
longitudinal design. When requesting dropout missing data, observations
are removed after a specific point in the time cycle. For example,
perhaps an individual stops being a part of the study after the third
measurement. To request this type of missing data, the
type = 'dropout' can be specified. In addition, one new
argument is needed, clust_var. This argument represents the
id associated with the clusters with longitudinal data. Below is an
example:
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
reg_weights = c(4, 0.5, 0.75, 0, 0.33),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
time_id = list(variance = 3, var_level = 2)),
missing_data = list(miss_prop = .25, new_outcome = 'y_missing',
clust_var = 'id', type = 'dropout'),
sample_size = list(level1 = 10, level2 = 20)
)
data_w_missing <- simulate_fixed(data = NULL, sim_arguments) |>
simulate_randomeffect(sim_arguments) |>
simulate_error(sim_arguments) |>
generate_response(sim_arguments) |>
generate_missing(sim_arguments)
head(data_w_missing, n = 10)## X.Intercept. time weight age treat_1 treat level1_id id int_id
## 1 1 0 231.1471 39 1 Control 1 1 0.2244243
## 2 1 1 158.6388 39 1 Control 2 1 0.2244243
## 3 1 2 171.6605 39 1 Control 3 1 0.2244243
## 4 1 3 176.4105 39 1 Control 4 1 0.2244243
## 5 1 4 176.2812 39 1 Control 5 1 0.2244243
## 6 1 5 188.0455 39 1 Control 6 1 0.2244243
## 7 1 6 201.8052 39 1 Control 7 1 0.2244243
## 8 1 7 186.9941 39 1 Control 8 1 0.2244243
## 9 1 8 190.1734 39 1 Control 9 1 0.2244243
## 10 1 9 163.4426 39 1 Control 10 1 0.2244243
## time_id error fixed_outcome random_effects y missing
## 1 2.533554 2.382697049 177.6903 0.2244243 180.2974 0
## 2 2.533554 -0.319179378 123.8091 2.7579779 126.2479 0
## 3 2.533554 0.716756305 134.0753 5.2915315 140.0836 0
## 4 2.533554 -0.314477357 138.1379 7.8250850 145.6485 0
## 5 2.533554 -0.325146339 138.5409 10.3586386 148.5744 0
## 6 2.533554 0.006078237 147.8641 12.8921922 160.7624 0
## 7 2.533554 1.286803356 158.6839 15.4257457 175.3965 0
## 8 2.533554 -1.039387080 148.0755 17.9592993 164.9955 0
## 9 2.533554 0.385799112 150.9601 20.4928529 171.8387 0
## 10 2.533554 1.927576531 131.4119 23.0264064 156.3659 0
## y_missing
## 1 180.2974
## 2 126.2479
## 3 140.0836
## 4 145.6485
## 5 148.5744
## 6 160.7624
## 7 175.3965
## 8 164.9955
## 9 171.8387
## 10 156.3659We can look at the amount of missing data:
prop.table(table(is.na(data_w_missing$y_missing)))##
## FALSE TRUE
## 0.945 0.055We can also look now at the amount of missing by the time variable.
prop.table(table(is.na(data_w_missing$y_missing), data_w_missing$time))##
## 0 1 2 3 4 5 6 7 8 9
## FALSE 0.100 0.100 0.100 0.100 0.095 0.090 0.090 0.090 0.090 0.090
## TRUE 0.000 0.000 0.000 0.000 0.005 0.010 0.010 0.010 0.010 0.010Specify location of dropout
If additional control is desired, specifying the location of the
dropout is possible. In this situation, a vector is passed to
dropout_location that specifies for each individual (more
generally each level 2 unit) where the dropout occurs. Below is an
example of this.
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
reg_weights = c(4, 0.5, 0.75, 0, 0.33),
fixed = list(time = list(var_type = 'time'),
weight = list(var_type = 'continuous', mean = 180, sd = 30),
age = list(var_type = 'ordinal', levels = 30:60, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
time_id = list(variance = 3, var_level = 2)),
missing_data = list(new_outcome = 'y_missing',
dropout_location = c(3, 9, 1, 6, 7, 8, 6, 9, 2, 4, 6, 5, 8, 9, 4, 5,
6, 7, 2, 9),
clust_var = 'id', type = 'dropout'),
sample_size = list(level1 = 10, level2 = 20)
)
data_w_missing <- simulate_fixed(data = NULL, sim_arguments) |>
simulate_randomeffect(sim_arguments) |>
simulate_error(sim_arguments) |>
generate_response(sim_arguments) |>
generate_missing(sim_arguments)
head(data_w_missing, n = 10)## X.Intercept. time weight age treat_1 treat level1_id id int_id
## 1 1 0 231.1471 39 1 Control 1 1 0.2244243
## 2 1 1 158.6388 39 1 Control 2 1 0.2244243
## 3 1 2 171.6605 39 1 Control 3 1 0.2244243
## 4 1 3 176.4105 39 1 Control 4 1 0.2244243
## 5 1 4 176.2812 39 1 Control 5 1 0.2244243
## 6 1 5 188.0455 39 1 Control 6 1 0.2244243
## 7 1 6 201.8052 39 1 Control 7 1 0.2244243
## 8 1 7 186.9941 39 1 Control 8 1 0.2244243
## 9 1 8 190.1734 39 1 Control 9 1 0.2244243
## 10 1 9 163.4426 39 1 Control 10 1 0.2244243
## time_id error fixed_outcome random_effects y missing
## 1 2.533554 2.382697049 177.6903 0.2244243 180.2974 0
## 2 2.533554 -0.319179378 123.8091 2.7579779 126.2479 0
## 3 2.533554 0.716756305 134.0753 5.2915315 140.0836 1
## 4 2.533554 -0.314477357 138.1379 7.8250850 145.6485 1
## 5 2.533554 -0.325146339 138.5409 10.3586386 148.5744 1
## 6 2.533554 0.006078237 147.8641 12.8921922 160.7624 1
## 7 2.533554 1.286803356 158.6839 15.4257457 175.3965 1
## 8 2.533554 -1.039387080 148.0755 17.9592993 164.9955 1
## 9 2.533554 0.385799112 150.9601 20.4928529 171.8387 1
## 10 2.533554 1.927576531 131.4119 23.0264064 156.3659 1
## y_missing
## 1 180.2974
## 2 126.2479
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## 7 NA
## 8 NA
## 9 NA
## 10 NAWe can look at the amount of missing data:
prop.table(table(is.na(data_w_missing$y_missing)))##
## FALSE TRUE
## 0.48 0.52We can also look now at the amount of missing by the time variable.
prop.table(table(is.na(data_w_missing$y_missing), data_w_missing$time))##
## 0 1 2 3 4 5 6 7 8 9
## FALSE 0.095 0.085 0.080 0.070 0.060 0.040 0.030 0.020 0.000 0.000
## TRUE 0.005 0.015 0.020 0.030 0.040 0.060 0.070 0.080 0.100 0.100Model Fit Arguments
The default behavior when fitting models to the generated data is to
fit a model with the same formula as the generated data
(i.e. formula simulation arguments). Secondly, the default
model functions are lm/glm for single level
simulation and lmer/glmer for multilevel
simulation. Finally, the default regression weights specified in
reg_weights are to compute statistics for power, type I
error rates, and precision.
These defaults can be overriden by specifying simulation arguments
through the named list called model_fit. Some examples were
given in the Tidy Simulation vignette and the options that are able to
be specified will depend on the model fitting function specified. A few
examples are given for various model types, however, the user is
directed to the documentation of the modeling functions specified
through the model_function argument.
Changing Family Argument
For GLM models, users may wish to directly specify the family argument or change the link function within a specific family. For example, when generating data with a binary dependent variable, the logistic or probit links could be used when specifying the binomial family. Below is an example of using the binomial family with a logit link (the default link function).
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + weight + age + sex,
fixed = list(weight = list(var_type = 'continuous', mean = 0, sd = 30),
age = list(var_type = 'ordinal', levels = 0:30),
sex = list(var_type = 'factor', levels = c('male', 'female'))),
error = list(variance = 25),
sample_size = 10,
reg_weights = c(2, 0.3, -0.1, 0.5),
outcome_type = 'binary',
model_fit = list(
model_function = 'glm',
family = binomial
)
)
tmp_data <- simulate_fixed(data = NULL, sim_arguments) |>
simulate_error(sim_arguments) |>
generate_response(sim_arguments) |>
model_fit(sim_arguments)
tmp_data$family##
## Family: binomial
## Link function: logitThe link function could then be changed to a probit link function with the following code:
set.seed(321)
sim_arguments <- list(
formula = y ~ 1 + weight + age + sex,
fixed = list(weight = list(var_type = 'continuous', mean = 0, sd = 30),
age = list(var_type = 'ordinal', levels = 0:30),
sex = list(var_type = 'factor', levels = c('male', 'female'))),
error = list(variance = 25),
sample_size = 10,
reg_weights = c(2, 0.3, -0.1, 0.5),
outcome_type = 'binary',
model_fit = list(
model_function = 'glm',
family = binomial(link = 'probit')
)
)
tmp_data <- simulate_fixed(data = NULL, sim_arguments) |>
simulate_error(sim_arguments) |>
generate_response(sim_arguments) |>
model_fit(sim_arguments)
tmp_data$family##
## Family: binomial
## Link function: probitAdding Serial Correlation
Serial correlation can be added to longitudinal designs and fitted using the nlme R package. Below is an example of implementing this functionality in the data generation and model fitting.
To come…
Marginal Models
It is also possible to change the default model fitting functions.
For example, an alternative model approach to mixed models is
generalized estimating equations (GEE) or marginal models. One
implementation of these models in R is the geepack package. Below is the
code to fit a marginal model in a longitudinal context. The code is not
evaluated, so no coefficients are shown, but if you have the
geepack package installed, this code should run as
written.
set.seed(321)
# To-DO: Add knot variable and debug
sim_arguments <- list(
formula = y ~ 1 + time + weight + age + treat + (1 + time| id),
fixed = list(time = list(var_type = 'time',
time_levels = c(0, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6)),
weight = list(var_type = 'continuous', mean = 0, sd = 30),
age = list(var_type = 'ordinal', levels = 0:30, var_level = 2),
treat = list(var_type = 'factor',
levels = c('Treatment', 'Control'),
var_level = 2)),
reg_weights = c(0.4, 0.2, -0.5, 1, -0.6),
randomeffect = list(int_id = list(variance = 8, var_level = 2),
time_id = list(variance = 3, var_level = 2)),
error = list(variance = 5),
outcome_type = 'binary',
sample_size = list(level1 = 10, level2 = 20),
model_fit = list(
model_function = geepack::geeglm,
formula = y ~ 1 + time + weight + age + treat,
id = 'level1_id',
family = binomial,
corstr = 'ar1'
)
)
simulate_fixed(data = NULL, sim_arguments) |>
simulate_error(sim_arguments) |>
simulate_randomeffect(sim_arguments) |>
generate_response(sim_arguments) |>
model_fit(sim_arguments) |>
extract_coefficients()Vary Simulation Arguments
The replicate_simulation function first shown in the
“tidy_simulation” vignette, can directly allow for the varying of
simulation arguments. In order to vary simulation arguments, an
additional named list called vary_arguments can be added to
the simulation arguments passed to the functions. The
vary_arguments elements of the simulation arguments is a
nested named list. Within the named list, the arguments that are wished
to be varied are specified by name. For example, a common argument
varied is sample size. The following example varies the sample size to
return multiple simulated data sets with different sample sizes.
Note: A user would likely want to change
plan(sequential) to something like
plan(mutisession) or plan(multicore) to run
these in parallel. plan(sequential) is used here for
vignette processing.
library(future)
plan(sequential)
sim_arguments <- list(
formula = resp_var ~ 1 + time + factor(trt) + time:factor(trt) +
(1 + time | individual),
reg_weights = c(4, 0.5, 0.75, 0),
fixed = list(time = list(var_type = 'time'),
trt = list(var_type = 'factor', levels = c('Drug', 'Placebo'),
var_level = 2)
),
randomeffect = list(int_clust = list(variance = .2, var_level = 2),
time_clust = list(variance = .3, var_level = 2)),
replications = 3,
error = list(variance = 1),
vary_arguments = list(
sample_size = list(list(level1 = 5, level2 = 50),
list(level1 = 5, level2 = 60))
)
)
replicate_simulation(sim_arguments)## [[1]]
## sample_size replication X.Intercept. time trt_1
## 1 list(level1 = 5, level2 = 50) 1 1 0 1
## 2 list(level1 = 5, level2 = 50) 1 1 1 1
## 3 list(level1 = 5, level2 = 50) 1 1 2 1
## 4 list(level1 = 5, level2 = 50) 1 1 3 1
## 5 list(level1 = 5, level2 = 50) 1 1 4 1
## 6 list(level1 = 5, level2 = 50) 1 1 0 1
## 7 list(level1 = 5, level2 = 50) 1 1 1 1
## 8 list(level1 = 5, level2 = 50) 1 1 2 1
## 9 list(level1 = 5, level2 = 50) 1 1 3 1
## 10 list(level1 = 5, level2 = 50) 1 1 4 1
## 11 list(level1 = 5, level2 = 50) 1 1 0 1
## 12 list(level1 = 5, level2 = 50) 1 1 1 1
## 13 list(level1 = 5, level2 = 50) 1 1 2 1
## 14 list(level1 = 5, level2 = 50) 1 1 3 1
## 15 list(level1 = 5, level2 = 50) 1 1 4 1
## 16 list(level1 = 5, level2 = 50) 1 1 0 1
## 17 list(level1 = 5, level2 = 50) 1 1 1 1
## 18 list(level1 = 5, level2 = 50) 1 1 2 1
## 19 list(level1 = 5, level2 = 50) 1 1 3 1
## 20 list(level1 = 5, level2 = 50) 1 1 4 1
## 21 list(level1 = 5, level2 = 50) 1 1 0 0
## 22 list(level1 = 5, level2 = 50) 1 1 1 0
## 23 list(level1 = 5, level2 = 50) 1 1 2 0
## 24 list(level1 = 5, level2 = 50) 1 1 3 0
## 25 list(level1 = 5, level2 = 50) 1 1 4 0
## 26 list(level1 = 5, level2 = 50) 1 1 0 0
## 27 list(level1 = 5, level2 = 50) 1 1 1 0
## 28 list(level1 = 5, level2 = 50) 1 1 2 0
## 29 list(level1 = 5, level2 = 50) 1 1 3 0
## 30 list(level1 = 5, level2 = 50) 1 1 4 0
## 31 list(level1 = 5, level2 = 50) 1 1 0 0
## 32 list(level1 = 5, level2 = 50) 1 1 1 0
## 33 list(level1 = 5, level2 = 50) 1 1 2 0
## 34 list(level1 = 5, level2 = 50) 1 1 3 0
## 35 list(level1 = 5, level2 = 50) 1 1 4 0
## 36 list(level1 = 5, level2 = 50) 1 1 0 0
## 37 list(level1 = 5, level2 = 50) 1 1 1 0
## 38 list(level1 = 5, level2 = 50) 1 1 2 0
## 39 list(level1 = 5, level2 = 50) 1 1 3 0
## 40 list(level1 = 5, level2 = 50) 1 1 4 0
## 41 list(level1 = 5, level2 = 50) 1 1 0 1
## 42 list(level1 = 5, level2 = 50) 1 1 1 1
## 43 list(level1 = 5, level2 = 50) 1 1 2 1
## 44 list(level1 = 5, level2 = 50) 1 1 3 1
## 45 list(level1 = 5, level2 = 50) 1 1 4 1
## 46 list(level1 = 5, level2 = 50) 1 1 0 0
## 47 list(level1 = 5, level2 = 50) 1 1 1 0
## 48 list(level1 = 5, level2 = 50) 1 1 2 0
## 49 list(level1 = 5, level2 = 50) 1 1 3 0
## 50 list(level1 = 5, level2 = 50) 1 1 4 0
## 51 list(level1 = 5, level2 = 50) 1 1 0 1
## 52 list(level1 = 5, level2 = 50) 1 1 1 1
## 53 list(level1 = 5, level2 = 50) 1 1 2 1
## 54 list(level1 = 5, level2 = 50) 1 1 3 1
## 55 list(level1 = 5, level2 = 50) 1 1 4 1
## 56 list(level1 = 5, level2 = 50) 1 1 0 0
## 57 list(level1 = 5, level2 = 50) 1 1 1 0
## 58 list(level1 = 5, level2 = 50) 1 1 2 0
## 59 list(level1 = 5, level2 = 50) 1 1 3 0
## 60 list(level1 = 5, level2 = 50) 1 1 4 0
## 61 list(level1 = 5, level2 = 50) 1 1 0 1
## 62 list(level1 = 5, level2 = 50) 1 1 1 1
## 63 list(level1 = 5, level2 = 50) 1 1 2 1
## 64 list(level1 = 5, level2 = 50) 1 1 3 1
## 65 list(level1 = 5, level2 = 50) 1 1 4 1
## 66 list(level1 = 5, level2 = 50) 1 1 0 0
## 67 list(level1 = 5, level2 = 50) 1 1 1 0
## 68 list(level1 = 5, level2 = 50) 1 1 2 0
## 69 list(level1 = 5, level2 = 50) 1 1 3 0
## 70 list(level1 = 5, level2 = 50) 1 1 4 0
## 71 list(level1 = 5, level2 = 50) 1 1 0 0
## 72 list(level1 = 5, level2 = 50) 1 1 1 0
## 73 list(level1 = 5, level2 = 50) 1 1 2 0
## 74 list(level1 = 5, level2 = 50) 1 1 3 0
## 75 list(level1 = 5, level2 = 50) 1 1 4 0
## 76 list(level1 = 5, level2 = 50) 1 1 0 0
## 77 list(level1 = 5, level2 = 50) 1 1 1 0
## 78 list(level1 = 5, level2 = 50) 1 1 2 0
## 79 list(level1 = 5, level2 = 50) 1 1 3 0
## 80 list(level1 = 5, level2 = 50) 1 1 4 0
## 81 list(level1 = 5, level2 = 50) 1 1 0 0
## 82 list(level1 = 5, level2 = 50) 1 1 1 0
## 83 list(level1 = 5, level2 = 50) 1 1 2 0
## 84 list(level1 = 5, level2 = 50) 1 1 3 0
## 85 list(level1 = 5, level2 = 50) 1 1 4 0
## 86 list(level1 = 5, level2 = 50) 1 1 0 1
## 87 list(level1 = 5, level2 = 50) 1 1 1 1
## 88 list(level1 = 5, level2 = 50) 1 1 2 1
## 89 list(level1 = 5, level2 = 50) 1 1 3 1
## 90 list(level1 = 5, level2 = 50) 1 1 4 1
## 91 list(level1 = 5, level2 = 50) 1 1 0 1
## 92 list(level1 = 5, level2 = 50) 1 1 1 1
## 93 list(level1 = 5, level2 = 50) 1 1 2 1
## 94 list(level1 = 5, level2 = 50) 1 1 3 1
## 95 list(level1 = 5, level2 = 50) 1 1 4 1
## 96 list(level1 = 5, level2 = 50) 1 1 0 1
## 97 list(level1 = 5, level2 = 50) 1 1 1 1
## 98 list(level1 = 5, level2 = 50) 1 1 2 1
## 99 list(level1 = 5, level2 = 50) 1 1 3 1
## 100 list(level1 = 5, level2 = 50) 1 1 4 1
## 101 list(level1 = 5, level2 = 50) 1 1 0 0
## 102 list(level1 = 5, level2 = 50) 1 1 1 0
## 103 list(level1 = 5, level2 = 50) 1 1 2 0
## 104 list(level1 = 5, level2 = 50) 1 1 3 0
## 105 list(level1 = 5, level2 = 50) 1 1 4 0
## 106 list(level1 = 5, level2 = 50) 1 1 0 1
## 107 list(level1 = 5, level2 = 50) 1 1 1 1
## 108 list(level1 = 5, level2 = 50) 1 1 2 1
## 109 list(level1 = 5, level2 = 50) 1 1 3 1
## 110 list(level1 = 5, level2 = 50) 1 1 4 1
## 111 list(level1 = 5, level2 = 50) 1 1 0 0
## 112 list(level1 = 5, level2 = 50) 1 1 1 0
## 113 list(level1 = 5, level2 = 50) 1 1 2 0
## 114 list(level1 = 5, level2 = 50) 1 1 3 0
## 115 list(level1 = 5, level2 = 50) 1 1 4 0
## 116 list(level1 = 5, level2 = 50) 1 1 0 0
## 117 list(level1 = 5, level2 = 50) 1 1 1 0
## 118 list(level1 = 5, level2 = 50) 1 1 2 0
## 119 list(level1 = 5, level2 = 50) 1 1 3 0
## 120 list(level1 = 5, level2 = 50) 1 1 4 0
## 121 list(level1 = 5, level2 = 50) 1 1 0 1
## 122 list(level1 = 5, level2 = 50) 1 1 1 1
## 123 list(level1 = 5, level2 = 50) 1 1 2 1
## 124 list(level1 = 5, level2 = 50) 1 1 3 1
## 125 list(level1 = 5, level2 = 50) 1 1 4 1
## 126 list(level1 = 5, level2 = 50) 1 1 0 0
## 127 list(level1 = 5, level2 = 50) 1 1 1 0
## 128 list(level1 = 5, level2 = 50) 1 1 2 0
## 129 list(level1 = 5, level2 = 50) 1 1 3 0
## 130 list(level1 = 5, level2 = 50) 1 1 4 0
## 131 list(level1 = 5, level2 = 50) 1 1 0 0
## 132 list(level1 = 5, level2 = 50) 1 1 1 0
## 133 list(level1 = 5, level2 = 50) 1 1 2 0
## 134 list(level1 = 5, level2 = 50) 1 1 3 0
## 135 list(level1 = 5, level2 = 50) 1 1 4 0
## 136 list(level1 = 5, level2 = 50) 1 1 0 0
## 137 list(level1 = 5, level2 = 50) 1 1 1 0
## 138 list(level1 = 5, level2 = 50) 1 1 2 0
## 139 list(level1 = 5, level2 = 50) 1 1 3 0
## 140 list(level1 = 5, level2 = 50) 1 1 4 0
## 141 list(level1 = 5, level2 = 50) 1 1 0 1
## 142 list(level1 = 5, level2 = 50) 1 1 1 1
## 143 list(level1 = 5, level2 = 50) 1 1 2 1
## 144 list(level1 = 5, level2 = 50) 1 1 3 1
## 145 list(level1 = 5, level2 = 50) 1 1 4 1
## 146 list(level1 = 5, level2 = 50) 1 1 0 1
## 147 list(level1 = 5, level2 = 50) 1 1 1 1
## 148 list(level1 = 5, level2 = 50) 1 1 2 1
## 149 list(level1 = 5, level2 = 50) 1 1 3 1
## 150 list(level1 = 5, level2 = 50) 1 1 4 1
## 151 list(level1 = 5, level2 = 50) 1 1 0 1
## 152 list(level1 = 5, level2 = 50) 1 1 1 1
## 153 list(level1 = 5, level2 = 50) 1 1 2 1
## 154 list(level1 = 5, level2 = 50) 1 1 3 1
## 155 list(level1 = 5, level2 = 50) 1 1 4 1
## 156 list(level1 = 5, level2 = 50) 1 1 0 0
## 157 list(level1 = 5, level2 = 50) 1 1 1 0
## 158 list(level1 = 5, level2 = 50) 1 1 2 0
## 159 list(level1 = 5, level2 = 50) 1 1 3 0
## 160 list(level1 = 5, level2 = 50) 1 1 4 0
## 161 list(level1 = 5, level2 = 50) 1 1 0 1
## 162 list(level1 = 5, level2 = 50) 1 1 1 1
## 163 list(level1 = 5, level2 = 50) 1 1 2 1
## 164 list(level1 = 5, level2 = 50) 1 1 3 1
## 165 list(level1 = 5, level2 = 50) 1 1 4 1
## 166 list(level1 = 5, level2 = 50) 1 1 0 1
## 167 list(level1 = 5, level2 = 50) 1 1 1 1
## 168 list(level1 = 5, level2 = 50) 1 1 2 1
## 169 list(level1 = 5, level2 = 50) 1 1 3 1
## 170 list(level1 = 5, level2 = 50) 1 1 4 1
## 171 list(level1 = 5, level2 = 50) 1 1 0 0
## 172 list(level1 = 5, level2 = 50) 1 1 1 0
## 173 list(level1 = 5, level2 = 50) 1 1 2 0
## 174 list(level1 = 5, level2 = 50) 1 1 3 0
## 175 list(level1 = 5, level2 = 50) 1 1 4 0
## 176 list(level1 = 5, level2 = 50) 1 1 0 1
## 177 list(level1 = 5, level2 = 50) 1 1 1 1
## 178 list(level1 = 5, level2 = 50) 1 1 2 1
## 179 list(level1 = 5, level2 = 50) 1 1 3 1
## 180 list(level1 = 5, level2 = 50) 1 1 4 1
## 181 list(level1 = 5, level2 = 50) 1 1 0 0
## 182 list(level1 = 5, level2 = 50) 1 1 1 0
## 183 list(level1 = 5, level2 = 50) 1 1 2 0
## 184 list(level1 = 5, level2 = 50) 1 1 3 0
## 185 list(level1 = 5, level2 = 50) 1 1 4 0
## 186 list(level1 = 5, level2 = 50) 1 1 0 1
## 187 list(level1 = 5, level2 = 50) 1 1 1 1
## 188 list(level1 = 5, level2 = 50) 1 1 2 1
## 189 list(level1 = 5, level2 = 50) 1 1 3 1
## 190 list(level1 = 5, level2 = 50) 1 1 4 1
## 191 list(level1 = 5, level2 = 50) 1 1 0 1
## 192 list(level1 = 5, level2 = 50) 1 1 1 1
## 193 list(level1 = 5, level2 = 50) 1 1 2 1
## 194 list(level1 = 5, level2 = 50) 1 1 3 1
## 195 list(level1 = 5, level2 = 50) 1 1 4 1
## 196 list(level1 = 5, level2 = 50) 1 1 0 0
## 197 list(level1 = 5, level2 = 50) 1 1 1 0
## 198 list(level1 = 5, level2 = 50) 1 1 2 0
## 199 list(level1 = 5, level2 = 50) 1 1 3 0
## 200 list(level1 = 5, level2 = 50) 1 1 4 0
## 201 list(level1 = 5, level2 = 50) 1 1 0 0
## 202 list(level1 = 5, level2 = 50) 1 1 1 0
## 203 list(level1 = 5, level2 = 50) 1 1 2 0
## 204 list(level1 = 5, level2 = 50) 1 1 3 0
## 205 list(level1 = 5, level2 = 50) 1 1 4 0
## 206 list(level1 = 5, level2 = 50) 1 1 0 0
## 207 list(level1 = 5, level2 = 50) 1 1 1 0
## 208 list(level1 = 5, level2 = 50) 1 1 2 0
## 209 list(level1 = 5, level2 = 50) 1 1 3 0
## 210 list(level1 = 5, level2 = 50) 1 1 4 0
## 211 list(level1 = 5, level2 = 50) 1 1 0 1
## 212 list(level1 = 5, level2 = 50) 1 1 1 1
## 213 list(level1 = 5, level2 = 50) 1 1 2 1
## 214 list(level1 = 5, level2 = 50) 1 1 3 1
## 215 list(level1 = 5, level2 = 50) 1 1 4 1
## 216 list(level1 = 5, level2 = 50) 1 1 0 0
## 217 list(level1 = 5, level2 = 50) 1 1 1 0
## 218 list(level1 = 5, level2 = 50) 1 1 2 0
## 219 list(level1 = 5, level2 = 50) 1 1 3 0
## 220 list(level1 = 5, level2 = 50) 1 1 4 0
## 221 list(level1 = 5, level2 = 50) 1 1 0 1
## 222 list(level1 = 5, level2 = 50) 1 1 1 1
## 223 list(level1 = 5, level2 = 50) 1 1 2 1
## 224 list(level1 = 5, level2 = 50) 1 1 3 1
## 225 list(level1 = 5, level2 = 50) 1 1 4 1
## 226 list(level1 = 5, level2 = 50) 1 1 0 0
## 227 list(level1 = 5, level2 = 50) 1 1 1 0
## 228 list(level1 = 5, level2 = 50) 1 1 2 0
## 229 list(level1 = 5, level2 = 50) 1 1 3 0
## 230 list(level1 = 5, level2 = 50) 1 1 4 0
## 231 list(level1 = 5, level2 = 50) 1 1 0 1
## 232 list(level1 = 5, level2 = 50) 1 1 1 1
## 233 list(level1 = 5, level2 = 50) 1 1 2 1
## 234 list(level1 = 5, level2 = 50) 1 1 3 1
## 235 list(level1 = 5, level2 = 50) 1 1 4 1
## 236 list(level1 = 5, level2 = 50) 1 1 0 0
## 237 list(level1 = 5, level2 = 50) 1 1 1 0
## 238 list(level1 = 5, level2 = 50) 1 1 2 0
## 239 list(level1 = 5, level2 = 50) 1 1 3 0
## 240 list(level1 = 5, level2 = 50) 1 1 4 0
## 241 list(level1 = 5, level2 = 50) 1 1 0 1
## 242 list(level1 = 5, level2 = 50) 1 1 1 1
## 243 list(level1 = 5, level2 = 50) 1 1 2 1
## 244 list(level1 = 5, level2 = 50) 1 1 3 1
## 245 list(level1 = 5, level2 = 50) 1 1 4 1
## 246 list(level1 = 5, level2 = 50) 1 1 0 1
## 247 list(level1 = 5, level2 = 50) 1 1 1 1
## 248 list(level1 = 5, level2 = 50) 1 1 2 1
## 249 list(level1 = 5, level2 = 50) 1 1 3 1
## 250 list(level1 = 5, level2 = 50) 1 1 4 1
## 251 list(level1 = 5, level2 = 50) 2 1 0 1
## 252 list(level1 = 5, level2 = 50) 2 1 1 1
## 253 list(level1 = 5, level2 = 50) 2 1 2 1
## 254 list(level1 = 5, level2 = 50) 2 1 3 1
## 255 list(level1 = 5, level2 = 50) 2 1 4 1
## 256 list(level1 = 5, level2 = 50) 2 1 0 0
## 257 list(level1 = 5, level2 = 50) 2 1 1 0
## 258 list(level1 = 5, level2 = 50) 2 1 2 0
## 259 list(level1 = 5, level2 = 50) 2 1 3 0
## 260 list(level1 = 5, level2 = 50) 2 1 4 0
## 261 list(level1 = 5, level2 = 50) 2 1 0 1
## 262 list(level1 = 5, level2 = 50) 2 1 1 1
## 263 list(level1 = 5, level2 = 50) 2 1 2 1
## 264 list(level1 = 5, level2 = 50) 2 1 3 1
## 265 list(level1 = 5, level2 = 50) 2 1 4 1
## 266 list(level1 = 5, level2 = 50) 2 1 0 0
## 267 list(level1 = 5, level2 = 50) 2 1 1 0
## 268 list(level1 = 5, level2 = 50) 2 1 2 0
## 269 list(level1 = 5, level2 = 50) 2 1 3 0
## 270 list(level1 = 5, level2 = 50) 2 1 4 0
## 271 list(level1 = 5, level2 = 50) 2 1 0 0
## 272 list(level1 = 5, level2 = 50) 2 1 1 0
## 273 list(level1 = 5, level2 = 50) 2 1 2 0
## 274 list(level1 = 5, level2 = 50) 2 1 3 0
## 275 list(level1 = 5, level2 = 50) 2 1 4 0
## 276 list(level1 = 5, level2 = 50) 2 1 0 1
## 277 list(level1 = 5, level2 = 50) 2 1 1 1
## 278 list(level1 = 5, level2 = 50) 2 1 2 1
## 279 list(level1 = 5, level2 = 50) 2 1 3 1
## 280 list(level1 = 5, level2 = 50) 2 1 4 1
## 281 list(level1 = 5, level2 = 50) 2 1 0 1
## 282 list(level1 = 5, level2 = 50) 2 1 1 1
## 283 list(level1 = 5, level2 = 50) 2 1 2 1
## 284 list(level1 = 5, level2 = 50) 2 1 3 1
## 285 list(level1 = 5, level2 = 50) 2 1 4 1
## 286 list(level1 = 5, level2 = 50) 2 1 0 0
## 287 list(level1 = 5, level2 = 50) 2 1 1 0
## 288 list(level1 = 5, level2 = 50) 2 1 2 0
## 289 list(level1 = 5, level2 = 50) 2 1 3 0
## 290 list(level1 = 5, level2 = 50) 2 1 4 0
## 291 list(level1 = 5, level2 = 50) 2 1 0 1
## 292 list(level1 = 5, level2 = 50) 2 1 1 1
## 293 list(level1 = 5, level2 = 50) 2 1 2 1
## 294 list(level1 = 5, level2 = 50) 2 1 3 1
## 295 list(level1 = 5, level2 = 50) 2 1 4 1
## 296 list(level1 = 5, level2 = 50) 2 1 0 0
## 297 list(level1 = 5, level2 = 50) 2 1 1 0
## 298 list(level1 = 5, level2 = 50) 2 1 2 0
## 299 list(level1 = 5, level2 = 50) 2 1 3 0
## 300 list(level1 = 5, level2 = 50) 2 1 4 0
## 301 list(level1 = 5, level2 = 50) 2 1 0 1
## 302 list(level1 = 5, level2 = 50) 2 1 1 1
## 303 list(level1 = 5, level2 = 50) 2 1 2 1
## 304 list(level1 = 5, level2 = 50) 2 1 3 1
## 305 list(level1 = 5, level2 = 50) 2 1 4 1
## 306 list(level1 = 5, level2 = 50) 2 1 0 1
## 307 list(level1 = 5, level2 = 50) 2 1 1 1
## 308 list(level1 = 5, level2 = 50) 2 1 2 1
## 309 list(level1 = 5, level2 = 50) 2 1 3 1
## 310 list(level1 = 5, level2 = 50) 2 1 4 1
## 311 list(level1 = 5, level2 = 50) 2 1 0 1
## 312 list(level1 = 5, level2 = 50) 2 1 1 1
## 313 list(level1 = 5, level2 = 50) 2 1 2 1
## 314 list(level1 = 5, level2 = 50) 2 1 3 1
## 315 list(level1 = 5, level2 = 50) 2 1 4 1
## 316 list(level1 = 5, level2 = 50) 2 1 0 0
## 317 list(level1 = 5, level2 = 50) 2 1 1 0
## 318 list(level1 = 5, level2 = 50) 2 1 2 0
## 319 list(level1 = 5, level2 = 50) 2 1 3 0
## 320 list(level1 = 5, level2 = 50) 2 1 4 0
## 321 list(level1 = 5, level2 = 50) 2 1 0 0
## 322 list(level1 = 5, level2 = 50) 2 1 1 0
## 323 list(level1 = 5, level2 = 50) 2 1 2 0
## 324 list(level1 = 5, level2 = 50) 2 1 3 0
## 325 list(level1 = 5, level2 = 50) 2 1 4 0
## 326 list(level1 = 5, level2 = 50) 2 1 0 0
## 327 list(level1 = 5, level2 = 50) 2 1 1 0
## 328 list(level1 = 5, level2 = 50) 2 1 2 0
## 329 list(level1 = 5, level2 = 50) 2 1 3 0
## 330 list(level1 = 5, level2 = 50) 2 1 4 0
## 331 list(level1 = 5, level2 = 50) 2 1 0 1
## 332 list(level1 = 5, level2 = 50) 2 1 1 1
## 333 list(level1 = 5, level2 = 50) 2 1 2 1
## 334 list(level1 = 5, level2 = 50) 2 1 3 1
## 335 list(level1 = 5, level2 = 50) 2 1 4 1
## 336 list(level1 = 5, level2 = 50) 2 1 0 1
## 337 list(level1 = 5, level2 = 50) 2 1 1 1
## 338 list(level1 = 5, level2 = 50) 2 1 2 1
## 339 list(level1 = 5, level2 = 50) 2 1 3 1
## 340 list(level1 = 5, level2 = 50) 2 1 4 1
## 341 list(level1 = 5, level2 = 50) 2 1 0 0
## 342 list(level1 = 5, level2 = 50) 2 1 1 0
## 343 list(level1 = 5, level2 = 50) 2 1 2 0
## 344 list(level1 = 5, level2 = 50) 2 1 3 0
## 345 list(level1 = 5, level2 = 50) 2 1 4 0
## 346 list(level1 = 5, level2 = 50) 2 1 0 1
## 347 list(level1 = 5, level2 = 50) 2 1 1 1
## 348 list(level1 = 5, level2 = 50) 2 1 2 1
## 349 list(level1 = 5, level2 = 50) 2 1 3 1
## 350 list(level1 = 5, level2 = 50) 2 1 4 1
## 351 list(level1 = 5, level2 = 50) 2 1 0 0
## 352 list(level1 = 5, level2 = 50) 2 1 1 0
## 353 list(level1 = 5, level2 = 50) 2 1 2 0
## 354 list(level1 = 5, level2 = 50) 2 1 3 0
## 355 list(level1 = 5, level2 = 50) 2 1 4 0
## 356 list(level1 = 5, level2 = 50) 2 1 0 0
## 357 list(level1 = 5, level2 = 50) 2 1 1 0
## 358 list(level1 = 5, level2 = 50) 2 1 2 0
## 359 list(level1 = 5, level2 = 50) 2 1 3 0
## 360 list(level1 = 5, level2 = 50) 2 1 4 0
## 361 list(level1 = 5, level2 = 50) 2 1 0 1
## 362 list(level1 = 5, level2 = 50) 2 1 1 1
## 363 list(level1 = 5, level2 = 50) 2 1 2 1
## 364 list(level1 = 5, level2 = 50) 2 1 3 1
## 365 list(level1 = 5, level2 = 50) 2 1 4 1
## 366 list(level1 = 5, level2 = 50) 2 1 0 0
## 367 list(level1 = 5, level2 = 50) 2 1 1 0
## 368 list(level1 = 5, level2 = 50) 2 1 2 0
## 369 list(level1 = 5, level2 = 50) 2 1 3 0
## 370 list(level1 = 5, level2 = 50) 2 1 4 0
## 371 list(level1 = 5, level2 = 50) 2 1 0 1
## 372 list(level1 = 5, level2 = 50) 2 1 1 1
## 373 list(level1 = 5, level2 = 50) 2 1 2 1
## 374 list(level1 = 5, level2 = 50) 2 1 3 1
## 375 list(level1 = 5, level2 = 50) 2 1 4 1
## 376 list(level1 = 5, level2 = 50) 2 1 0 0
## 377 list(level1 = 5, level2 = 50) 2 1 1 0
## 378 list(level1 = 5, level2 = 50) 2 1 2 0
## 379 list(level1 = 5, level2 = 50) 2 1 3 0
## 380 list(level1 = 5, level2 = 50) 2 1 4 0
## 381 list(level1 = 5, level2 = 50) 2 1 0 1
## 382 list(level1 = 5, level2 = 50) 2 1 1 1
## 383 list(level1 = 5, level2 = 50) 2 1 2 1
## 384 list(level1 = 5, level2 = 50) 2 1 3 1
## 385 list(level1 = 5, level2 = 50) 2 1 4 1
## 386 list(level1 = 5, level2 = 50) 2 1 0 0
## 387 list(level1 = 5, level2 = 50) 2 1 1 0
## 388 list(level1 = 5, level2 = 50) 2 1 2 0
## 389 list(level1 = 5, level2 = 50) 2 1 3 0
## 390 list(level1 = 5, level2 = 50) 2 1 4 0
## 391 list(level1 = 5, level2 = 50) 2 1 0 0
## 392 list(level1 = 5, level2 = 50) 2 1 1 0
## 393 list(level1 = 5, level2 = 50) 2 1 2 0
## 394 list(level1 = 5, level2 = 50) 2 1 3 0
## 395 list(level1 = 5, level2 = 50) 2 1 4 0
## 396 list(level1 = 5, level2 = 50) 2 1 0 1
## 397 list(level1 = 5, level2 = 50) 2 1 1 1
## 398 list(level1 = 5, level2 = 50) 2 1 2 1
## 399 list(level1 = 5, level2 = 50) 2 1 3 1
## 400 list(level1 = 5, level2 = 50) 2 1 4 1
## 401 list(level1 = 5, level2 = 50) 2 1 0 1
## 402 list(level1 = 5, level2 = 50) 2 1 1 1
## 403 list(level1 = 5, level2 = 50) 2 1 2 1
## 404 list(level1 = 5, level2 = 50) 2 1 3 1
## 405 list(level1 = 5, level2 = 50) 2 1 4 1
## 406 list(level1 = 5, level2 = 50) 2 1 0 0
## 407 list(level1 = 5, level2 = 50) 2 1 1 0
## 408 list(level1 = 5, level2 = 50) 2 1 2 0
## 409 list(level1 = 5, level2 = 50) 2 1 3 0
## 410 list(level1 = 5, level2 = 50) 2 1 4 0
## 411 list(level1 = 5, level2 = 50) 2 1 0 0
## 412 list(level1 = 5, level2 = 50) 2 1 1 0
## 413 list(level1 = 5, level2 = 50) 2 1 2 0
## 414 list(level1 = 5, level2 = 50) 2 1 3 0
## 415 list(level1 = 5, level2 = 50) 2 1 4 0
## 416 list(level1 = 5, level2 = 50) 2 1 0 0
## 417 list(level1 = 5, level2 = 50) 2 1 1 0
## 418 list(level1 = 5, level2 = 50) 2 1 2 0
## 419 list(level1 = 5, level2 = 50) 2 1 3 0
## 420 list(level1 = 5, level2 = 50) 2 1 4 0
## 421 list(level1 = 5, level2 = 50) 2 1 0 1
## 422 list(level1 = 5, level2 = 50) 2 1 1 1
## 423 list(level1 = 5, level2 = 50) 2 1 2 1
## 424 list(level1 = 5, level2 = 50) 2 1 3 1
## 425 list(level1 = 5, level2 = 50) 2 1 4 1
## 426 list(level1 = 5, level2 = 50) 2 1 0 1
## 427 list(level1 = 5, level2 = 50) 2 1 1 1
## 428 list(level1 = 5, level2 = 50) 2 1 2 1
## 429 list(level1 = 5, level2 = 50) 2 1 3 1
## 430 list(level1 = 5, level2 = 50) 2 1 4 1
## 431 list(level1 = 5, level2 = 50) 2 1 0 1
## 432 list(level1 = 5, level2 = 50) 2 1 1 1
## 433 list(level1 = 5, level2 = 50) 2 1 2 1
## 434 list(level1 = 5, level2 = 50) 2 1 3 1
## 435 list(level1 = 5, level2 = 50) 2 1 4 1
## 436 list(level1 = 5, level2 = 50) 2 1 0 0
## 437 list(level1 = 5, level2 = 50) 2 1 1 0
## 438 list(level1 = 5, level2 = 50) 2 1 2 0
## 439 list(level1 = 5, level2 = 50) 2 1 3 0
## 440 list(level1 = 5, level2 = 50) 2 1 4 0
## 441 list(level1 = 5, level2 = 50) 2 1 0 1
## 442 list(level1 = 5, level2 = 50) 2 1 1 1
## 443 list(level1 = 5, level2 = 50) 2 1 2 1
## 444 list(level1 = 5, level2 = 50) 2 1 3 1
## 445 list(level1 = 5, level2 = 50) 2 1 4 1
## 446 list(level1 = 5, level2 = 50) 2 1 0 1
## 447 list(level1 = 5, level2 = 50) 2 1 1 1
## 448 list(level1 = 5, level2 = 50) 2 1 2 1
## 449 list(level1 = 5, level2 = 50) 2 1 3 1
## 450 list(level1 = 5, level2 = 50) 2 1 4 1
## 451 list(level1 = 5, level2 = 50) 2 1 0 1
## 452 list(level1 = 5, level2 = 50) 2 1 1 1
## 453 list(level1 = 5, level2 = 50) 2 1 2 1
## 454 list(level1 = 5, level2 = 50) 2 1 3 1
## 455 list(level1 = 5, level2 = 50) 2 1 4 1
## 456 list(level1 = 5, level2 = 50) 2 1 0 0
## 457 list(level1 = 5, level2 = 50) 2 1 1 0
## 458 list(level1 = 5, level2 = 50) 2 1 2 0
## 459 list(level1 = 5, level2 = 50) 2 1 3 0
## 460 list(level1 = 5, level2 = 50) 2 1 4 0
## 461 list(level1 = 5, level2 = 50) 2 1 0 0
## 462 list(level1 = 5, level2 = 50) 2 1 1 0
## 463 list(level1 = 5, level2 = 50) 2 1 2 0
## 464 list(level1 = 5, level2 = 50) 2 1 3 0
## 465 list(level1 = 5, level2 = 50) 2 1 4 0
## 466 list(level1 = 5, level2 = 50) 2 1 0 0
## 467 list(level1 = 5, level2 = 50) 2 1 1 0
## 468 list(level1 = 5, level2 = 50) 2 1 2 0
## 469 list(level1 = 5, level2 = 50) 2 1 3 0
## 470 list(level1 = 5, level2 = 50) 2 1 4 0
## 471 list(level1 = 5, level2 = 50) 2 1 0 1
## 472 list(level1 = 5, level2 = 50) 2 1 1 1
## 473 list(level1 = 5, level2 = 50) 2 1 2 1
## 474 list(level1 = 5, level2 = 50) 2 1 3 1
## 475 list(level1 = 5, level2 = 50) 2 1 4 1
## 476 list(level1 = 5, level2 = 50) 2 1 0 0
## 477 list(level1 = 5, level2 = 50) 2 1 1 0
## 478 list(level1 = 5, level2 = 50) 2 1 2 0
## 479 list(level1 = 5, level2 = 50) 2 1 3 0
## 480 list(level1 = 5, level2 = 50) 2 1 4 0
## 481 list(level1 = 5, level2 = 50) 2 1 0 1
## 482 list(level1 = 5, level2 = 50) 2 1 1 1
## 483 list(level1 = 5, level2 = 50) 2 1 2 1
## 484 list(level1 = 5, level2 = 50) 2 1 3 1
## 485 list(level1 = 5, level2 = 50) 2 1 4 1
## 486 list(level1 = 5, level2 = 50) 2 1 0 0
## 487 list(level1 = 5, level2 = 50) 2 1 1 0
## 488 list(level1 = 5, level2 = 50) 2 1 2 0
## 489 list(level1 = 5, level2 = 50) 2 1 3 0
## 490 list(level1 = 5, level2 = 50) 2 1 4 0
## 491 list(level1 = 5, level2 = 50) 2 1 0 1
## 492 list(level1 = 5, level2 = 50) 2 1 1 1
## 493 list(level1 = 5, level2 = 50) 2 1 2 1
## 494 list(level1 = 5, level2 = 50) 2 1 3 1
## 495 list(level1 = 5, level2 = 50) 2 1 4 1
## 496 list(level1 = 5, level2 = 50) 2 1 0 1
## 497 list(level1 = 5, level2 = 50) 2 1 1 1
## 498 list(level1 = 5, level2 = 50) 2 1 2 1
## 499 list(level1 = 5, level2 = 50) 2 1 3 1
## 500 list(level1 = 5, level2 = 50) 2 1 4 1
## 501 list(level1 = 5, level2 = 50) 3 1 0 0
## 502 list(level1 = 5, level2 = 50) 3 1 1 0
## 503 list(level1 = 5, level2 = 50) 3 1 2 0
## 504 list(level1 = 5, level2 = 50) 3 1 3 0
## 505 list(level1 = 5, level2 = 50) 3 1 4 0
## 506 list(level1 = 5, level2 = 50) 3 1 0 0
## 507 list(level1 = 5, level2 = 50) 3 1 1 0
## 508 list(level1 = 5, level2 = 50) 3 1 2 0
## 509 list(level1 = 5, level2 = 50) 3 1 3 0
## 510 list(level1 = 5, level2 = 50) 3 1 4 0
## 511 list(level1 = 5, level2 = 50) 3 1 0 1
## 512 list(level1 = 5, level2 = 50) 3 1 1 1
## 513 list(level1 = 5, level2 = 50) 3 1 2 1
## 514 list(level1 = 5, level2 = 50) 3 1 3 1
## 515 list(level1 = 5, level2 = 50) 3 1 4 1
## 516 list(level1 = 5, level2 = 50) 3 1 0 0
## 517 list(level1 = 5, level2 = 50) 3 1 1 0
## 518 list(level1 = 5, level2 = 50) 3 1 2 0
## 519 list(level1 = 5, level2 = 50) 3 1 3 0
## 520 list(level1 = 5, level2 = 50) 3 1 4 0
## 521 list(level1 = 5, level2 = 50) 3 1 0 0
## 522 list(level1 = 5, level2 = 50) 3 1 1 0
## 523 list(level1 = 5, level2 = 50) 3 1 2 0
## 524 list(level1 = 5, level2 = 50) 3 1 3 0
## 525 list(level1 = 5, level2 = 50) 3 1 4 0
## 526 list(level1 = 5, level2 = 50) 3 1 0 1
## 527 list(level1 = 5, level2 = 50) 3 1 1 1
## 528 list(level1 = 5, level2 = 50) 3 1 2 1
## 529 list(level1 = 5, level2 = 50) 3 1 3 1
## 530 list(level1 = 5, level2 = 50) 3 1 4 1
## 531 list(level1 = 5, level2 = 50) 3 1 0 1
## 532 list(level1 = 5, level2 = 50) 3 1 1 1
## 533 list(level1 = 5, level2 = 50) 3 1 2 1
## 534 list(level1 = 5, level2 = 50) 3 1 3 1
## 535 list(level1 = 5, level2 = 50) 3 1 4 1
## 536 list(level1 = 5, level2 = 50) 3 1 0 1
## 537 list(level1 = 5, level2 = 50) 3 1 1 1
## 538 list(level1 = 5, level2 = 50) 3 1 2 1
## 539 list(level1 = 5, level2 = 50) 3 1 3 1
## 540 list(level1 = 5, level2 = 50) 3 1 4 1
## 541 list(level1 = 5, level2 = 50) 3 1 0 0
## 542 list(level1 = 5, level2 = 50) 3 1 1 0
## 543 list(level1 = 5, level2 = 50) 3 1 2 0
## 544 list(level1 = 5, level2 = 50) 3 1 3 0
## 545 list(level1 = 5, level2 = 50) 3 1 4 0
## 546 list(level1 = 5, level2 = 50) 3 1 0 1
## 547 list(level1 = 5, level2 = 50) 3 1 1 1
## 548 list(level1 = 5, level2 = 50) 3 1 2 1
## 549 list(level1 = 5, level2 = 50) 3 1 3 1
## 550 list(level1 = 5, level2 = 50) 3 1 4 1
## 551 list(level1 = 5, level2 = 50) 3 1 0 1
## 552 list(level1 = 5, level2 = 50) 3 1 1 1
## 553 list(level1 = 5, level2 = 50) 3 1 2 1
## 554 list(level1 = 5, level2 = 50) 3 1 3 1
## 555 list(level1 = 5, level2 = 50) 3 1 4 1
## 556 list(level1 = 5, level2 = 50) 3 1 0 0
## 557 list(level1 = 5, level2 = 50) 3 1 1 0
## 558 list(level1 = 5, level2 = 50) 3 1 2 0
## 559 list(level1 = 5, level2 = 50) 3 1 3 0
## 560 list(level1 = 5, level2 = 50) 3 1 4 0
## 561 list(level1 = 5, level2 = 50) 3 1 0 0
## 562 list(level1 = 5, level2 = 50) 3 1 1 0
## 563 list(level1 = 5, level2 = 50) 3 1 2 0
## 564 list(level1 = 5, level2 = 50) 3 1 3 0
## 565 list(level1 = 5, level2 = 50) 3 1 4 0
## 566 list(level1 = 5, level2 = 50) 3 1 0 0
## 567 list(level1 = 5, level2 = 50) 3 1 1 0
## 568 list(level1 = 5, level2 = 50) 3 1 2 0
## 569 list(level1 = 5, level2 = 50) 3 1 3 0
## 570 list(level1 = 5, level2 = 50) 3 1 4 0
## 571 list(level1 = 5, level2 = 50) 3 1 0 1
## 572 list(level1 = 5, level2 = 50) 3 1 1 1
## 573 list(level1 = 5, level2 = 50) 3 1 2 1
## 574 list(level1 = 5, level2 = 50) 3 1 3 1
## 575 list(level1 = 5, level2 = 50) 3 1 4 1
## 576 list(level1 = 5, level2 = 50) 3 1 0 1
## 577 list(level1 = 5, level2 = 50) 3 1 1 1
## 578 list(level1 = 5, level2 = 50) 3 1 2 1
## 579 list(level1 = 5, level2 = 50) 3 1 3 1
## 580 list(level1 = 5, level2 = 50) 3 1 4 1
## 581 list(level1 = 5, level2 = 50) 3 1 0 1
## 582 list(level1 = 5, level2 = 50) 3 1 1 1
## 583 list(level1 = 5, level2 = 50) 3 1 2 1
## 584 list(level1 = 5, level2 = 50) 3 1 3 1
## 585 list(level1 = 5, level2 = 50) 3 1 4 1
## 586 list(level1 = 5, level2 = 50) 3 1 0 0
## 587 list(level1 = 5, level2 = 50) 3 1 1 0
## 588 list(level1 = 5, level2 = 50) 3 1 2 0
## 589 list(level1 = 5, level2 = 50) 3 1 3 0
## 590 list(level1 = 5, level2 = 50) 3 1 4 0
## 591 list(level1 = 5, level2 = 50) 3 1 0 0
## 592 list(level1 = 5, level2 = 50) 3 1 1 0
## 593 list(level1 = 5, level2 = 50) 3 1 2 0
## 594 list(level1 = 5, level2 = 50) 3 1 3 0
## 595 list(level1 = 5, level2 = 50) 3 1 4 0
## 596 list(level1 = 5, level2 = 50) 3 1 0 0
## 597 list(level1 = 5, level2 = 50) 3 1 1 0
## 598 list(level1 = 5, level2 = 50) 3 1 2 0
## 599 list(level1 = 5, level2 = 50) 3 1 3 0
## 600 list(level1 = 5, level2 = 50) 3 1 4 0
## 601 list(level1 = 5, level2 = 50) 3 1 0 1
## 602 list(level1 = 5, level2 = 50) 3 1 1 1
## 603 list(level1 = 5, level2 = 50) 3 1 2 1
## 604 list(level1 = 5, level2 = 50) 3 1 3 1
## 605 list(level1 = 5, level2 = 50) 3 1 4 1
## 606 list(level1 = 5, level2 = 50) 3 1 0 1
## 607 list(level1 = 5, level2 = 50) 3 1 1 1
## 608 list(level1 = 5, level2 = 50) 3 1 2 1
## 609 list(level1 = 5, level2 = 50) 3 1 3 1
## 610 list(level1 = 5, level2 = 50) 3 1 4 1
## 611 list(level1 = 5, level2 = 50) 3 1 0 0
## 612 list(level1 = 5, level2 = 50) 3 1 1 0
## 613 list(level1 = 5, level2 = 50) 3 1 2 0
## 614 list(level1 = 5, level2 = 50) 3 1 3 0
## 615 list(level1 = 5, level2 = 50) 3 1 4 0
## 616 list(level1 = 5, level2 = 50) 3 1 0 0
## 617 list(level1 = 5, level2 = 50) 3 1 1 0
## 618 list(level1 = 5, level2 = 50) 3 1 2 0
## 619 list(level1 = 5, level2 = 50) 3 1 3 0
## 620 list(level1 = 5, level2 = 50) 3 1 4 0
## 621 list(level1 = 5, level2 = 50) 3 1 0 1
## 622 list(level1 = 5, level2 = 50) 3 1 1 1
## 623 list(level1 = 5, level2 = 50) 3 1 2 1
## 624 list(level1 = 5, level2 = 50) 3 1 3 1
## 625 list(level1 = 5, level2 = 50) 3 1 4 1
## 626 list(level1 = 5, level2 = 50) 3 1 0 1
## 627 list(level1 = 5, level2 = 50) 3 1 1 1
## 628 list(level1 = 5, level2 = 50) 3 1 2 1
## 629 list(level1 = 5, level2 = 50) 3 1 3 1
## 630 list(level1 = 5, level2 = 50) 3 1 4 1
## 631 list(level1 = 5, level2 = 50) 3 1 0 1
## 632 list(level1 = 5, level2 = 50) 3 1 1 1
## 633 list(level1 = 5, level2 = 50) 3 1 2 1
## 634 list(level1 = 5, level2 = 50) 3 1 3 1
## 635 list(level1 = 5, level2 = 50) 3 1 4 1
## 636 list(level1 = 5, level2 = 50) 3 1 0 0
## 637 list(level1 = 5, level2 = 50) 3 1 1 0
## 638 list(level1 = 5, level2 = 50) 3 1 2 0
## 639 list(level1 = 5, level2 = 50) 3 1 3 0
## 640 list(level1 = 5, level2 = 50) 3 1 4 0
## 641 list(level1 = 5, level2 = 50) 3 1 0 0
## 642 list(level1 = 5, level2 = 50) 3 1 1 0
## 643 list(level1 = 5, level2 = 50) 3 1 2 0
## 644 list(level1 = 5, level2 = 50) 3 1 3 0
## 645 list(level1 = 5, level2 = 50) 3 1 4 0
## 646 list(level1 = 5, level2 = 50) 3 1 0 0
## 647 list(level1 = 5, level2 = 50) 3 1 1 0
## 648 list(level1 = 5, level2 = 50) 3 1 2 0
## 649 list(level1 = 5, level2 = 50) 3 1 3 0
## 650 list(level1 = 5, level2 = 50) 3 1 4 0
## 651 list(level1 = 5, level2 = 50) 3 1 0 1
## 652 list(level1 = 5, level2 = 50) 3 1 1 1
## 653 list(level1 = 5, level2 = 50) 3 1 2 1
## 654 list(level1 = 5, level2 = 50) 3 1 3 1
## 655 list(level1 = 5, level2 = 50) 3 1 4 1
## 656 list(level1 = 5, level2 = 50) 3 1 0 0
## 657 list(level1 = 5, level2 = 50) 3 1 1 0
## 658 list(level1 = 5, level2 = 50) 3 1 2 0
## 659 list(level1 = 5, level2 = 50) 3 1 3 0
## 660 list(level1 = 5, level2 = 50) 3 1 4 0
## 661 list(level1 = 5, level2 = 50) 3 1 0 0
## 662 list(level1 = 5, level2 = 50) 3 1 1 0
## 663 list(level1 = 5, level2 = 50) 3 1 2 0
## 664 list(level1 = 5, level2 = 50) 3 1 3 0
## 665 list(level1 = 5, level2 = 50) 3 1 4 0
## 666 list(level1 = 5, level2 = 50) 3 1 0 1
## 667 list(level1 = 5, level2 = 50) 3 1 1 1
## 668 list(level1 = 5, level2 = 50) 3 1 2 1
## 669 list(level1 = 5, level2 = 50) 3 1 3 1
## 670 list(level1 = 5, level2 = 50) 3 1 4 1
## 671 list(level1 = 5, level2 = 50) 3 1 0 0
## 672 list(level1 = 5, level2 = 50) 3 1 1 0
## 673 list(level1 = 5, level2 = 50) 3 1 2 0
## 674 list(level1 = 5, level2 = 50) 3 1 3 0
## 675 list(level1 = 5, level2 = 50) 3 1 4 0
## 676 list(level1 = 5, level2 = 50) 3 1 0 0
## 677 list(level1 = 5, level2 = 50) 3 1 1 0
## 678 list(level1 = 5, level2 = 50) 3 1 2 0
## 679 list(level1 = 5, level2 = 50) 3 1 3 0
## 680 list(level1 = 5, level2 = 50) 3 1 4 0
## 681 list(level1 = 5, level2 = 50) 3 1 0 1
## 682 list(level1 = 5, level2 = 50) 3 1 1 1
## 683 list(level1 = 5, level2 = 50) 3 1 2 1
## 684 list(level1 = 5, level2 = 50) 3 1 3 1
## 685 list(level1 = 5, level2 = 50) 3 1 4 1
## 686 list(level1 = 5, level2 = 50) 3 1 0 0
## 687 list(level1 = 5, level2 = 50) 3 1 1 0
## 688 list(level1 = 5, level2 = 50) 3 1 2 0
## 689 list(level1 = 5, level2 = 50) 3 1 3 0
## 690 list(level1 = 5, level2 = 50) 3 1 4 0
## 691 list(level1 = 5, level2 = 50) 3 1 0 0
## 692 list(level1 = 5, level2 = 50) 3 1 1 0
## 693 list(level1 = 5, level2 = 50) 3 1 2 0
## 694 list(level1 = 5, level2 = 50) 3 1 3 0
## 695 list(level1 = 5, level2 = 50) 3 1 4 0
## 696 list(level1 = 5, level2 = 50) 3 1 0 1
## 697 list(level1 = 5, level2 = 50) 3 1 1 1
## 698 list(level1 = 5, level2 = 50) 3 1 2 1
## 699 list(level1 = 5, level2 = 50) 3 1 3 1
## 700 list(level1 = 5, level2 = 50) 3 1 4 1
## 701 list(level1 = 5, level2 = 50) 3 1 0 0
## 702 list(level1 = 5, level2 = 50) 3 1 1 0
## 703 list(level1 = 5, level2 = 50) 3 1 2 0
## 704 list(level1 = 5, level2 = 50) 3 1 3 0
## 705 list(level1 = 5, level2 = 50) 3 1 4 0
## 706 list(level1 = 5, level2 = 50) 3 1 0 1
## 707 list(level1 = 5, level2 = 50) 3 1 1 1
## 708 list(level1 = 5, level2 = 50) 3 1 2 1
## 709 list(level1 = 5, level2 = 50) 3 1 3 1
## 710 list(level1 = 5, level2 = 50) 3 1 4 1
## 711 list(level1 = 5, level2 = 50) 3 1 0 0
## 712 list(level1 = 5, level2 = 50) 3 1 1 0
## 713 list(level1 = 5, level2 = 50) 3 1 2 0
## 714 list(level1 = 5, level2 = 50) 3 1 3 0
## 715 list(level1 = 5, level2 = 50) 3 1 4 0
## 716 list(level1 = 5, level2 = 50) 3 1 0 1
## 717 list(level1 = 5, level2 = 50) 3 1 1 1
## 718 list(level1 = 5, level2 = 50) 3 1 2 1
## 719 list(level1 = 5, level2 = 50) 3 1 3 1
## 720 list(level1 = 5, level2 = 50) 3 1 4 1
## 721 list(level1 = 5, level2 = 50) 3 1 0 0
## 722 list(level1 = 5, level2 = 50) 3 1 1 0
## 723 list(level1 = 5, level2 = 50) 3 1 2 0
## 724 list(level1 = 5, level2 = 50) 3 1 3 0
## 725 list(level1 = 5, level2 = 50) 3 1 4 0
## 726 list(level1 = 5, level2 = 50) 3 1 0 1
## 727 list(level1 = 5, level2 = 50) 3 1 1 1
## 728 list(level1 = 5, level2 = 50) 3 1 2 1
## 729 list(level1 = 5, level2 = 50) 3 1 3 1
## 730 list(level1 = 5, level2 = 50) 3 1 4 1
## 731 list(level1 = 5, level2 = 50) 3 1 0 1
## 732 list(level1 = 5, level2 = 50) 3 1 1 1
## 733 list(level1 = 5, level2 = 50) 3 1 2 1
## 734 list(level1 = 5, level2 = 50) 3 1 3 1
## 735 list(level1 = 5, level2 = 50) 3 1 4 1
## 736 list(level1 = 5, level2 = 50) 3 1 0 1
## 737 list(level1 = 5, level2 = 50) 3 1 1 1
## 738 list(level1 = 5, level2 = 50) 3 1 2 1
## 739 list(level1 = 5, level2 = 50) 3 1 3 1
## 740 list(level1 = 5, level2 = 50) 3 1 4 1
## 741 list(level1 = 5, level2 = 50) 3 1 0 1
## 742 list(level1 = 5, level2 = 50) 3 1 1 1
## 743 list(level1 = 5, level2 = 50) 3 1 2 1
## 744 list(level1 = 5, level2 = 50) 3 1 3 1
## 745 list(level1 = 5, level2 = 50) 3 1 4 1
## 746 list(level1 = 5, level2 = 50) 3 1 0 0
## 747 list(level1 = 5, level2 = 50) 3 1 1 0
## 748 list(level1 = 5, level2 = 50) 3 1 2 0
## 749 list(level1 = 5, level2 = 50) 3 1 3 0
## 750 list(level1 = 5, level2 = 50) 3 1 4 0
## time.trt_1 trt level1_id individual error int_clust
## 1 0 Placebo 1 1 -0.997949680 -0.133309572
## 2 1 Placebo 2 1 -2.255614876 -0.133309572
## 3 2 Placebo 3 1 0.110959440 -0.133309572
## 4 3 Placebo 4 1 -0.280874551 -0.133309572
## 5 4 Placebo 5 1 0.038809113 -0.133309572
## 6 0 Placebo 1 2 -1.594275507 -0.480881684
## 7 1 Placebo 2 2 0.212687071 -0.480881684
## 8 2 Placebo 3 2 -0.772604493 -0.480881684
## 9 3 Placebo 4 2 -1.006839719 -0.480881684
## 10 4 Placebo 5 2 0.910036681 -0.480881684
## 11 0 Placebo 1 3 -1.662349695 -0.736020469
## 12 1 Placebo 2 3 -1.444442890 -0.736020469
## 13 2 Placebo 3 3 -0.067811833 -0.736020469
## 14 3 Placebo 4 3 0.791580261 -0.736020469
## 15 4 Placebo 5 3 0.249352566 -0.736020469
## 16 0 Placebo 1 4 -0.540337359 0.143262419
## 17 1 Placebo 2 4 -0.175486388 0.143262419
## 18 2 Placebo 3 4 0.378628121 0.143262419
## 19 3 Placebo 4 4 0.399578812 0.143262419
## 20 4 Placebo 5 4 0.448409378 0.143262419
## 21 0 Drug 1 5 -1.153710020 -0.160175802
## 22 0 Drug 2 5 -0.562871185 -0.160175802
## 23 0 Drug 3 5 -1.026357669 -0.160175802
## 24 0 Drug 4 5 -0.047109817 -0.160175802
## 25 0 Drug 5 5 -0.350057188 -0.160175802
## 26 0 Drug 1 6 -0.154669219 -0.443390013
## 27 0 Drug 2 6 -0.765995066 -0.443390013
## 28 0 Drug 3 6 0.092484284 -0.443390013
## 29 0 Drug 4 6 -0.965295290 -0.443390013
## 30 0 Drug 5 6 -1.083832899 -0.443390013
## 31 0 Drug 1 7 -0.514971481 0.036217487
## 32 0 Drug 2 7 -0.138036257 0.036217487
## 33 0 Drug 3 7 -1.727194118 0.036217487
## 34 0 Drug 4 7 -1.874741507 0.036217487
## 35 0 Drug 5 7 -0.083105222 0.036217487
## 36 0 Drug 1 8 -0.577655627 -0.345947633
## 37 0 Drug 2 8 -0.251361488 -0.345947633
## 38 0 Drug 3 8 0.489879849 -0.345947633
## 39 0 Drug 4 8 -0.452346277 -0.345947633
## 40 0 Drug 5 8 -0.022021200 -0.345947633
## 41 0 Placebo 1 9 0.995558731 -0.078905065
## 42 1 Placebo 2 9 -0.292893513 -0.078905065
## 43 2 Placebo 3 9 -0.248453397 -0.078905065
## 44 3 Placebo 4 9 -0.193792643 -0.078905065
## 45 4 Placebo 5 9 -1.120965280 -0.078905065
## 46 0 Drug 1 10 0.032284229 0.053371343
## 47 0 Drug 2 10 -0.090430522 0.053371343
## 48 0 Drug 3 10 -0.288400607 0.053371343
## 49 0 Drug 4 10 -0.926483537 0.053371343
## 50 0 Drug 5 10 0.864339568 0.053371343
## 51 0 Placebo 1 11 1.024682664 0.362051027
## 52 1 Placebo 2 11 -0.202619028 0.362051027
## 53 2 Placebo 3 11 0.287712921 0.362051027
## 54 3 Placebo 4 11 1.082988393 0.362051027
## 55 4 Placebo 5 11 -0.779095851 0.362051027
## 56 0 Drug 1 12 -0.208420002 0.229762013
## 57 0 Drug 2 12 -0.169491211 0.229762013
## 58 0 Drug 3 12 0.571111971 0.229762013
## 59 0 Drug 4 12 0.069291152 0.229762013
## 60 0 Drug 5 12 0.479061763 0.229762013
## 61 0 Placebo 1 13 -1.908567021 -0.131871072
## 62 1 Placebo 2 13 1.234902933 -0.131871072
## 63 2 Placebo 3 13 0.951533285 -0.131871072
## 64 3 Placebo 4 13 -0.371611714 -0.131871072
## 65 4 Placebo 5 13 0.529979464 -0.131871072
## 66 0 Drug 1 14 0.105861467 0.011547218
## 67 0 Drug 2 14 0.135521337 0.011547218
## 68 0 Drug 3 14 0.971355976 0.011547218
## 69 0 Drug 4 14 0.523890397 0.011547218
## 70 0 Drug 5 14 1.509586337 0.011547218
## 71 0 Drug 1 15 0.275473520 0.044868934
## 72 0 Drug 2 15 -1.111799513 0.044868934
## 73 0 Drug 3 15 0.844893438 0.044868934
## 74 0 Drug 4 15 -0.062621692 0.044868934
## 75 0 Drug 5 15 0.045931508 0.044868934
## 76 0 Drug 1 16 0.302486117 0.635596716
## 77 0 Drug 2 16 0.238139826 0.635596716
## 78 0 Drug 3 16 -1.546631115 0.635596716
## 79 0 Drug 4 16 -0.175958574 0.635596716
## 80 0 Drug 5 16 1.139433409 0.635596716
## 81 0 Drug 1 17 0.412971011 0.509775750
## 82 0 Drug 2 17 -0.740631950 0.509775750
## 83 0 Drug 3 17 1.285260943 0.509775750
## 84 0 Drug 4 17 -2.536792964 0.509775750
## 85 0 Drug 5 17 -0.283747800 0.509775750
## 86 0 Placebo 1 18 0.435201760 0.247462363
## 87 1 Placebo 2 18 0.301316657 0.247462363
## 88 2 Placebo 3 18 -0.915453627 0.247462363
## 89 3 Placebo 4 18 -1.775341603 0.247462363
## 90 4 Placebo 5 18 2.773141880 0.247462363
## 91 0 Placebo 1 19 -0.066371158 0.282389552
## 92 1 Placebo 2 19 -1.105414273 0.282389552
## 93 2 Placebo 3 19 -0.771187025 0.282389552
## 94 3 Placebo 4 19 1.011618958 0.282389552
## 95 4 Placebo 5 19 0.817579473 0.282389552
## 96 0 Placebo 1 20 0.659511353 1.085859427
## 97 1 Placebo 2 20 0.022006725 1.085859427
## 98 2 Placebo 3 20 0.558676279 1.085859427
## 99 3 Placebo 4 20 -2.015449238 1.085859427
## 100 4 Placebo 5 20 -0.825226710 1.085859427
## 101 0 Drug 1 21 1.622315125 0.746236413
## 102 0 Drug 2 21 -0.522126454 0.746236413
## 103 0 Drug 3 21 -1.105471186 0.746236413
## 104 0 Drug 4 21 -0.200929829 0.746236413
## 105 0 Drug 5 21 0.073524746 0.746236413
## 106 0 Placebo 1 22 -0.097710095 -0.301850134
## 107 1 Placebo 2 22 0.243938127 -0.301850134
## 108 2 Placebo 3 22 -0.101047075 -0.301850134
## 109 3 Placebo 4 22 1.388938324 -0.301850134
## 110 4 Placebo 5 22 -1.972582900 -0.301850134
## 111 0 Drug 1 23 1.359638406 0.027250019
## 112 0 Drug 2 23 0.262219482 0.027250019
## 113 0 Drug 3 23 -0.596971405 0.027250019
## 114 0 Drug 4 23 -0.358152561 0.027250019
## 115 0 Drug 5 23 -1.354973675 0.027250019
## 116 0 Drug 1 24 0.262714119 0.193099183
## 117 0 Drug 2 24 -0.275887359 0.193099183
## 118 0 Drug 3 24 -0.249920254 0.193099183
## 119 0 Drug 4 24 0.161664969 0.193099183
## 120 0 Drug 5 24 -0.600420530 0.193099183
## 121 0 Placebo 1 25 0.640848891 0.423161150
## 122 1 Placebo 2 25 -0.585236523 0.423161150
## 123 2 Placebo 3 25 0.779364022 0.423161150
## 124 3 Placebo 4 25 -0.651961618 0.423161150
## 125 4 Placebo 5 25 0.723196169 0.423161150
## 126 0 Drug 1 26 1.289117959 0.849509032
## 127 0 Drug 2 26 1.261839805 0.849509032
## 128 0 Drug 3 26 -1.129675002 0.849509032
## 129 0 Drug 4 26 -0.508875686 0.849509032
## 130 0 Drug 5 26 -0.340067025 0.849509032
## 131 0 Drug 1 27 -1.530466217 0.212263770
## 132 0 Drug 2 27 2.867379807 0.212263770
## 133 0 Drug 3 27 -0.171778913 0.212263770
## 134 0 Drug 4 27 1.189416174 0.212263770
## 135 0 Drug 5 27 0.194829119 0.212263770
## 136 0 Drug 1 28 1.039996641 -0.839626706
## 137 0 Drug 2 28 -1.825158958 -0.839626706
## 138 0 Drug 3 28 0.230727879 -0.839626706
## 139 0 Drug 4 28 0.392459914 -0.839626706
## 140 0 Drug 5 28 0.001953359 -0.839626706
## 141 0 Placebo 1 29 -0.898404930 0.387808131
## 142 1 Placebo 2 29 -1.840926070 0.387808131
## 143 2 Placebo 3 29 1.110438733 0.387808131
## 144 3 Placebo 4 29 -1.396112078 0.387808131
## 145 4 Placebo 5 29 0.172441899 0.387808131
## 146 0 Placebo 1 30 -0.745539002 0.063263201
## 147 1 Placebo 2 30 -0.276934923 0.063263201
## 148 2 Placebo 3 30 0.246672629 0.063263201
## 149 3 Placebo 4 30 -0.323723697 0.063263201
## 150 4 Placebo 5 30 0.406751967 0.063263201
## 151 0 Placebo 1 31 -0.161178868 -0.364292681
## 152 1 Placebo 2 31 -1.419257561 -0.364292681
## 153 2 Placebo 3 31 0.629838045 -0.364292681
## 154 3 Placebo 4 31 -0.834877422 -0.364292681
## 155 4 Placebo 5 31 0.411562239 -0.364292681
## 156 0 Drug 1 32 0.797935853 0.730069636
## 157 0 Drug 2 32 -0.149011469 0.730069636
## 158 0 Drug 3 32 -0.814076423 0.730069636
## 159 0 Drug 4 32 1.339611249 0.730069636
## 160 0 Drug 5 32 -1.702523094 0.730069636
## 161 0 Placebo 1 33 -0.770968241 -1.321235982
## 162 1 Placebo 2 33 2.040523989 -1.321235982
## 163 2 Placebo 3 33 0.548069906 -1.321235982
## 164 3 Placebo 4 33 0.621362087 -1.321235982
## 165 4 Placebo 5 33 -1.007660867 -1.321235982
## 166 0 Placebo 1 34 1.336712375 0.014014635
## 167 1 Placebo 2 34 1.435848136 0.014014635
## 168 2 Placebo 3 34 0.252853399 0.014014635
## 169 3 Placebo 4 34 -1.301169531 0.014014635
## 170 4 Placebo 5 34 0.338305093 0.014014635
## 171 0 Drug 1 35 0.034651552 -0.384578997
## 172 0 Drug 2 35 0.419931019 -0.384578997
## 173 0 Drug 3 35 -1.276356933 -0.384578997
## 174 0 Drug 4 35 -1.851367240 -0.384578997
## 175 0 Drug 5 35 0.083726401 -0.384578997
## 176 0 Placebo 1 36 1.474944700 -0.252253130
## 177 1 Placebo 2 36 -0.063975296 -0.252253130
## 178 2 Placebo 3 36 -0.709924145 -0.252253130
## 179 3 Placebo 4 36 -0.774660208 -0.252253130
## 180 4 Placebo 5 36 1.658125361 -0.252253130
## 181 0 Drug 1 37 1.166360585 -0.260349802
## 182 0 Drug 2 37 2.805874408 -0.260349802
## 183 0 Drug 3 37 0.477716907 -0.260349802
## 184 0 Drug 4 37 0.565624489 -0.260349802
## 185 0 Drug 5 37 0.025222050 -0.260349802
## 186 0 Placebo 1 38 -0.409547221 -0.347638233
## 187 1 Placebo 2 38 0.067748831 -0.347638233
## 188 2 Placebo 3 38 -0.330412052 -0.347638233
## 189 3 Placebo 4 38 0.259452489 -0.347638233
## 190 4 Placebo 5 38 0.174131289 -0.347638233
## 191 0 Placebo 1 39 0.815599179 -0.069338374
## 192 1 Placebo 2 39 -1.050238062 -0.069338374
## 193 2 Placebo 3 39 0.366835746 -0.069338374
## 194 3 Placebo 4 39 -0.822293783 -0.069338374
## 195 4 Placebo 5 39 2.116453534 -0.069338374
## 196 0 Drug 1 40 0.508182060 0.595054526
## 197 0 Drug 2 40 0.555343938 0.595054526
## 198 0 Drug 3 40 -0.744027707 0.595054526
## 199 0 Drug 4 40 0.410007823 0.595054526
## 200 0 Drug 5 40 0.155663940 0.595054526
## 201 0 Drug 1 41 0.243044709 0.061469318
## 202 0 Drug 2 41 0.624469227 0.061469318
## 203 0 Drug 3 41 -0.636682688 0.061469318
## 204 0 Drug 4 41 -0.933315044 0.061469318
## 205 0 Drug 5 41 -0.679269746 0.061469318
## 206 0 Drug 1 42 -0.160249819 0.104091589
## 207 0 Drug 2 42 -0.449427405 0.104091589
## 208 0 Drug 3 42 -1.051503364 0.104091589
## 209 0 Drug 4 42 0.586452579 0.104091589
## 210 0 Drug 5 42 1.502654600 0.104091589
## 211 0 Placebo 1 43 0.998136462 -0.258308276
## 212 1 Placebo 2 43 0.628352552 -0.258308276
## 213 2 Placebo 3 43 0.145465108 -0.258308276
## 214 3 Placebo 4 43 -0.440616662 -0.258308276
## 215 4 Placebo 5 43 -0.536034920 -0.258308276
## 216 0 Drug 1 44 -0.174865804 0.691305034
## 217 0 Drug 2 44 0.981850964 0.691305034
## 218 0 Drug 3 44 -2.053633319 0.691305034
## 219 0 Drug 4 44 0.693343355 0.691305034
## 220 0 Drug 5 44 0.833608910 0.691305034
## 221 0 Placebo 1 45 0.488270201 -0.285642285
## 222 1 Placebo 2 45 -1.878676340 -0.285642285
## 223 2 Placebo 3 45 -0.032131575 -0.285642285
## 224 3 Placebo 4 45 1.415339856 -0.285642285
## 225 4 Placebo 5 45 0.017498246 -0.285642285
## 226 0 Drug 1 46 1.054522731 0.663897278
## 227 0 Drug 2 46 0.545207301 0.663897278
## 228 0 Drug 3 46 1.080341461 0.663897278
## 229 0 Drug 4 46 -1.965242098 0.663897278
## 230 0 Drug 5 46 -0.474668640 0.663897278
## 231 0 Placebo 1 47 1.408040083 0.008318249
## 232 1 Placebo 2 47 -0.806974613 0.008318249
## 233 2 Placebo 3 47 -1.205271887 0.008318249
## 234 3 Placebo 4 47 -1.758983276 0.008318249
## 235 4 Placebo 5 47 -0.133694828 0.008318249
## 236 0 Drug 1 48 1.474494051 0.688783288
## 237 0 Drug 2 48 2.016028117 0.688783288
## 238 0 Drug 3 48 0.097870744 0.688783288
## 239 0 Drug 4 48 1.740961890 0.688783288
## 240 0 Drug 5 48 -1.076052841 0.688783288
## 241 0 Placebo 1 49 1.358402668 0.320057166
## 242 1 Placebo 2 49 0.851292921 0.320057166
## 243 2 Placebo 3 49 0.068935953 0.320057166
## 244 3 Placebo 4 49 0.139831635 0.320057166
## 245 4 Placebo 5 49 -0.818191773 0.320057166
## 246 0 Placebo 1 50 1.052431116 0.008098378
## 247 1 Placebo 2 50 -0.176855908 0.008098378
## 248 2 Placebo 3 50 0.942668654 0.008098378
## 249 3 Placebo 4 50 -0.775208553 0.008098378
## 250 4 Placebo 5 50 -1.031502492 0.008098378
## 251 0 Placebo 1 1 -1.290031433 -0.565234685
## 252 1 Placebo 2 1 0.285492674 -0.565234685
## 253 2 Placebo 3 1 -1.254083355 -0.565234685
## 254 3 Placebo 4 1 0.231899272 -0.565234685
## 255 4 Placebo 5 1 1.319354837 -0.565234685
## 256 0 Drug 1 2 0.674093651 -0.371996787
## 257 0 Drug 2 2 0.023815332 -0.371996787
## 258 0 Drug 3 2 1.360686074 -0.371996787
## 259 0 Drug 4 2 -0.935764936 -0.371996787
## 260 0 Drug 5 2 2.329041221 -0.371996787
## 261 0 Placebo 1 3 0.340602896 -0.458478922
## 262 1 Placebo 2 3 0.725177393 -0.458478922
## 263 2 Placebo 3 3 -0.960882499 -0.458478922
## 264 3 Placebo 4 3 -1.207128339 -0.458478922
## 265 4 Placebo 5 3 0.272145672 -0.458478922
## 266 0 Drug 1 4 0.357931865 0.035206844
## 267 0 Drug 2 4 -1.194210775 0.035206844
## 268 0 Drug 3 4 -0.056787594 0.035206844
## 269 0 Drug 4 4 0.730992347 0.035206844
## 270 0 Drug 5 4 -0.049314959 0.035206844
## 271 0 Drug 1 5 -0.661708612 -0.264679787
## 272 0 Drug 2 5 0.884855267 -0.264679787
## 273 0 Drug 3 5 -1.100506663 -0.264679787
## 274 0 Drug 4 5 -0.645467078 -0.264679787
## 275 0 Drug 5 5 1.173695189 -0.264679787
## 276 0 Placebo 1 6 0.470545516 0.146650685
## 277 1 Placebo 2 6 -0.205453775 0.146650685
## 278 2 Placebo 3 6 0.406852706 0.146650685
## 279 3 Placebo 4 6 -0.004473205 0.146650685
## 280 4 Placebo 5 6 0.704974727 0.146650685
## 281 0 Placebo 1 7 0.674162926 0.692517404
## 282 1 Placebo 2 7 0.201880819 0.692517404
## 283 2 Placebo 3 7 0.754295090 0.692517404
## 284 3 Placebo 4 7 -0.690732015 0.692517404
## 285 4 Placebo 5 7 -0.695734885 0.692517404
## 286 0 Drug 1 8 0.379220504 -0.305347074
## 287 0 Drug 2 8 0.147541702 -0.305347074
## 288 0 Drug 3 8 -0.533056707 -0.305347074
## 289 0 Drug 4 8 -0.640392258 -0.305347074
## 290 0 Drug 5 8 -0.404886582 -0.305347074
## 291 0 Placebo 1 9 -1.271718326 0.768908304
## 292 1 Placebo 2 9 -0.459294425 0.768908304
## 293 2 Placebo 3 9 -1.315742965 0.768908304
## 294 3 Placebo 4 9 1.084603931 0.768908304
## 295 4 Placebo 5 9 1.195465813 0.768908304
## 296 0 Drug 1 10 -1.485815643 -0.190301484
## 297 0 Drug 2 10 0.845103958 -0.190301484
## 298 0 Drug 3 10 2.574462578 -0.190301484
## 299 0 Drug 4 10 -0.394568661 -0.190301484
## 300 0 Drug 5 10 -0.412709904 -0.190301484
## 301 0 Placebo 1 11 -0.215546435 0.296637706
## 302 1 Placebo 2 11 -0.556045622 0.296637706
## 303 2 Placebo 3 11 0.819119345 0.296637706
## 304 3 Placebo 4 11 -0.648590537 0.296637706
## 305 4 Placebo 5 11 1.078036764 0.296637706
## 306 0 Placebo 1 12 -0.604434152 0.416943211
## 307 1 Placebo 2 12 0.528534698 0.416943211
## 308 2 Placebo 3 12 0.376537680 0.416943211
## 309 3 Placebo 4 12 -0.133819684 0.416943211
## 310 4 Placebo 5 12 -1.552224054 0.416943211
## 311 0 Placebo 1 13 -0.466816111 -0.461701554
## 312 1 Placebo 2 13 0.381216208 -0.461701554
## 313 2 Placebo 3 13 0.062065321 -0.461701554
## 314 3 Placebo 4 13 0.408208229 -0.461701554
## 315 4 Placebo 5 13 0.563605276 -0.461701554
## 316 0 Drug 1 14 -0.155664015 0.011572720
## 317 0 Drug 2 14 0.239623191 0.011572720
## 318 0 Drug 3 14 -0.761408126 0.011572720
## 319 0 Drug 4 14 0.274373132 0.011572720
## 320 0 Drug 5 14 1.511149707 0.011572720
## 321 0 Drug 1 15 -0.384583469 -0.145885895
## 322 0 Drug 2 15 -1.456882580 -0.145885895
## 323 0 Drug 3 15 -0.887006631 -0.145885895
## 324 0 Drug 4 15 -0.569469471 -0.145885895
## 325 0 Drug 5 15 1.370147718 -0.145885895
## 326 0 Drug 1 16 0.240912297 0.081351747
## 327 0 Drug 2 16 -0.160090880 0.081351747
## 328 0 Drug 3 16 -0.006932120 0.081351747
## 329 0 Drug 4 16 -0.621463042 0.081351747
## 330 0 Drug 5 16 -0.589485440 0.081351747
## 331 0 Placebo 1 17 1.307180384 -0.422444659
## 332 1 Placebo 2 17 1.348013941 -0.422444659
## 333 2 Placebo 3 17 0.238592618 -0.422444659
## 334 3 Placebo 4 17 -0.225324249 -0.422444659
## 335 4 Placebo 5 17 1.190907024 -0.422444659
## 336 0 Placebo 1 18 -1.087453407 -0.008155262
## 337 1 Placebo 2 18 -0.299055573 -0.008155262
## 338 2 Placebo 3 18 -0.740475535 -0.008155262
## 339 3 Placebo 4 18 -0.630044782 -0.008155262
## 340 4 Placebo 5 18 0.856654394 -0.008155262
## 341 0 Drug 1 19 -0.982095936 0.194698886
## 342 0 Drug 2 19 -0.153204054 0.194698886
## 343 0 Drug 3 19 -0.097358132 0.194698886
## 344 0 Drug 4 19 0.304459030 0.194698886
## 345 0 Drug 5 19 -0.583927395 0.194698886
## 346 0 Placebo 1 20 0.434087447 -0.234107100
## 347 1 Placebo 2 20 -0.761082093 -0.234107100
## 348 2 Placebo 3 20 0.169280600 -0.234107100
## 349 3 Placebo 4 20 0.330987393 -0.234107100
## 350 4 Placebo 5 20 -0.399756186 -0.234107100
## 351 0 Drug 1 21 0.613166237 0.215944689
## 352 0 Drug 2 21 1.154727916 0.215944689
## 353 0 Drug 3 21 2.016940867 0.215944689
## 354 0 Drug 4 21 0.177020465 0.215944689
## 355 0 Drug 5 21 -0.620038960 0.215944689
## 356 0 Drug 1 22 0.653912967 0.075519297
## 357 0 Drug 2 22 1.176850956 0.075519297
## 358 0 Drug 3 22 -0.517376872 0.075519297
## 359 0 Drug 4 22 0.718000143 0.075519297
## 360 0 Drug 5 22 0.716983182 0.075519297
## 361 0 Placebo 1 23 0.311655196 -0.211276496
## 362 1 Placebo 2 23 1.260658746 -0.211276496
## 363 2 Placebo 3 23 0.592969498 -0.211276496
## 364 3 Placebo 4 23 -0.823849128 -0.211276496
## 365 4 Placebo 5 23 -0.764163354 -0.211276496
## 366 0 Drug 1 24 0.373613447 -0.764920052
## 367 0 Drug 2 24 -1.267644148 -0.764920052
## 368 0 Drug 3 24 0.894172721 -0.764920052
## 369 0 Drug 4 24 -2.298926303 -0.764920052
## 370 0 Drug 5 24 -0.614981865 -0.764920052
## 371 0 Placebo 1 25 -1.330389274 -0.719292038
## 372 1 Placebo 2 25 0.125832227 -0.719292038
## 373 2 Placebo 3 25 1.023328054 -0.719292038
## 374 3 Placebo 4 25 0.235346769 -0.719292038
## 375 4 Placebo 5 25 0.238832454 -0.719292038
## 376 0 Drug 1 26 -0.643888523 -0.033238584
## 377 0 Drug 2 26 0.966267263 -0.033238584
## 378 0 Drug 3 26 -0.239763816 -0.033238584
## 379 0 Drug 4 26 -0.105003785 -0.033238584
## 380 0 Drug 5 26 -0.864099894 -0.033238584
## 381 0 Placebo 1 27 -0.915820726 0.487078316
## 382 1 Placebo 2 27 0.048832529 0.487078316
## 383 2 Placebo 3 27 1.751195998 0.487078316
## 384 3 Placebo 4 27 0.173242746 0.487078316
## 385 4 Placebo 5 27 -1.090488421 0.487078316
## 386 0 Drug 1 28 -0.451675479 0.379337927
## 387 0 Drug 2 28 0.358795474 0.379337927
## 388 0 Drug 3 28 1.326871263 0.379337927
## 389 0 Drug 4 28 1.510263343 0.379337927
## 390 0 Drug 5 28 1.182290793 0.379337927
## 391 0 Drug 1 29 0.931125208 0.192200137
## 392 0 Drug 2 29 -0.847326106 0.192200137
## 393 0 Drug 3 29 -0.706603547 0.192200137
## 394 0 Drug 4 29 -0.443387961 0.192200137
## 395 0 Drug 5 29 1.313314053 0.192200137
## 396 0 Placebo 1 30 1.108166615 -0.317077819
## 397 1 Placebo 2 30 0.161332207 -0.317077819
## 398 2 Placebo 3 30 0.221791972 -0.317077819
## 399 3 Placebo 4 30 0.232492353 -0.317077819
## 400 4 Placebo 5 30 -1.451001583 -0.317077819
## 401 0 Placebo 1 31 3.000164063 0.690643872
## 402 1 Placebo 2 31 -0.897779675 0.690643872
## 403 2 Placebo 3 31 -0.981978187 0.690643872
## 404 3 Placebo 4 31 -2.238392697 0.690643872
## 405 4 Placebo 5 31 1.003094392 0.690643872
## 406 0 Drug 1 32 0.725599331 0.525731010
## 407 0 Drug 2 32 -0.892427255 0.525731010
## 408 0 Drug 3 32 -0.087648633 0.525731010
## 409 0 Drug 4 32 0.871317585 0.525731010
## 410 0 Drug 5 32 -1.173374131 0.525731010
## 411 0 Drug 1 33 0.840410669 0.375234504
## 412 0 Drug 2 33 0.007795460 0.375234504
## 413 0 Drug 3 33 -0.179038362 0.375234504
## 414 0 Drug 4 33 0.130960837 0.375234504
## 415 0 Drug 5 33 1.177197610 0.375234504
## 416 0 Drug 1 34 -1.499253643 -0.021032067
## 417 0 Drug 2 34 0.808329175 -0.021032067
## 418 0 Drug 3 34 -0.505689996 -0.021032067
## 419 0 Drug 4 34 0.023368683 -0.021032067
## 420 0 Drug 5 34 -1.318401375 -0.021032067
## 421 0 Placebo 1 35 -0.106636821 0.691188727
## 422 1 Placebo 2 35 0.470324760 0.691188727
## 423 2 Placebo 3 35 -0.921234940 0.691188727
## 424 3 Placebo 4 35 -0.616604202 0.691188727
## 425 4 Placebo 5 35 0.174475460 0.691188727
## 426 0 Placebo 1 36 0.436895467 0.184582155
## 427 1 Placebo 2 36 1.419655600 0.184582155
## 428 2 Placebo 3 36 -0.230931061 0.184582155
## 429 3 Placebo 4 36 -0.134219551 0.184582155
## 430 4 Placebo 5 36 -0.118693870 0.184582155
## 431 0 Placebo 1 37 0.224204354 0.872153307
## 432 1 Placebo 2 37 2.542728203 0.872153307
## 433 2 Placebo 3 37 -0.416960698 0.872153307
## 434 3 Placebo 4 37 -0.154399122 0.872153307
## 435 4 Placebo 5 37 1.580708689 0.872153307
## 436 0 Drug 1 38 -0.750705334 -0.163662341
## 437 0 Drug 2 38 0.109312794 -0.163662341
## 438 0 Drug 3 38 -0.649475416 -0.163662341
## 439 0 Drug 4 38 -1.112094892 -0.163662341
## 440 0 Drug 5 38 0.118523448 -0.163662341
## 441 0 Placebo 1 39 -0.023356960 0.970063397
## 442 1 Placebo 2 39 1.836138033 0.970063397
## 443 2 Placebo 3 39 0.333392620 0.970063397
## 444 3 Placebo 4 39 -0.256638160 0.970063397
## 445 4 Placebo 5 39 0.458408281 0.970063397
## 446 0 Placebo 1 40 0.288549437 0.344425634
## 447 1 Placebo 2 40 0.165056560 0.344425634
## 448 2 Placebo 3 40 -0.507568093 0.344425634
## 449 3 Placebo 4 40 -0.866680140 0.344425634
## 450 4 Placebo 5 40 1.396650758 0.344425634
## 451 0 Placebo 1 41 2.415623169 -0.084575223
## 452 1 Placebo 2 41 0.272221081 -0.084575223
## 453 2 Placebo 3 41 0.350584641 -0.084575223
## 454 3 Placebo 4 41 0.465623598 -0.084575223
## 455 4 Placebo 5 41 -0.359524842 -0.084575223
## 456 0 Drug 1 42 -0.309588625 0.567198904
## 457 0 Drug 2 42 -1.259521020 0.567198904
## 458 0 Drug 3 42 -0.487881158 0.567198904
## 459 0 Drug 4 42 0.662870470 0.567198904
## 460 0 Drug 5 42 -0.990646580 0.567198904
## 461 0 Drug 1 43 -0.813765036 -0.273659201
## 462 0 Drug 2 43 -1.385855470 -0.273659201
## 463 0 Drug 3 43 0.379999508 -0.273659201
## 464 0 Drug 4 43 -0.357702336 -0.273659201
## 465 0 Drug 5 43 -0.962461398 -0.273659201
## 466 0 Drug 1 44 -0.852168169 -0.074708299
## 467 0 Drug 2 44 0.786809764 -0.074708299
## 468 0 Drug 3 44 -1.603629645 -0.074708299
## 469 0 Drug 4 44 -0.873527453 -0.074708299
## 470 0 Drug 5 44 1.057460915 -0.074708299
## 471 0 Placebo 1 45 -0.275635696 0.139210262
## 472 1 Placebo 2 45 1.750003931 0.139210262
## 473 2 Placebo 3 45 -1.041559057 0.139210262
## 474 3 Placebo 4 45 0.482777507 0.139210262
## 475 4 Placebo 5 45 -1.318233185 0.139210262
## 476 0 Drug 1 46 -0.412642644 0.026828911
## 477 0 Drug 2 46 -0.348903671 0.026828911
## 478 0 Drug 3 46 -0.804650974 0.026828911
## 479 0 Drug 4 46 -0.383549016 0.026828911
## 480 0 Drug 5 46 0.871283475 0.026828911
## 481 0 Placebo 1 47 0.857368307 -0.677526042
## 482 1 Placebo 2 47 0.645771602 -0.677526042
## 483 2 Placebo 3 47 0.518024944 -0.677526042
## 484 3 Placebo 4 47 -0.704615927 -0.677526042
## 485 4 Placebo 5 47 0.554758013 -0.677526042
## 486 0 Drug 1 48 -0.637652644 0.913488290
## 487 0 Drug 2 48 -0.727779660 0.913488290
## 488 0 Drug 3 48 -0.612974308 0.913488290
## 489 0 Drug 4 48 0.341622052 0.913488290
## 490 0 Drug 5 48 -1.674393277 0.913488290
## 491 0 Placebo 1 49 0.678637605 1.229789315
## 492 1 Placebo 2 49 0.894145141 1.229789315
## 493 2 Placebo 3 49 -0.068815890 1.229789315
## 494 3 Placebo 4 49 -1.177057464 1.229789315
## 495 4 Placebo 5 49 -1.502283494 1.229789315
## 496 0 Placebo 1 50 0.148368759 0.510155158
## 497 1 Placebo 2 50 0.034261396 0.510155158
## 498 2 Placebo 3 50 1.102344436 0.510155158
## 499 3 Placebo 4 50 -0.868060551 0.510155158
## 500 4 Placebo 5 50 0.266441997 0.510155158
## 501 0 Drug 1 1 -1.023301939 -0.609667122
## 502 0 Drug 2 1 1.023318642 -0.609667122
## 503 0 Drug 3 1 1.088765240 -0.609667122
## 504 0 Drug 4 1 0.398708443 -0.609667122
## 505 0 Drug 5 1 1.088779846 -0.609667122
## 506 0 Drug 1 2 0.597995432 0.000470811
## 507 0 Drug 2 2 0.613196294 0.000470811
## 508 0 Drug 3 2 -0.542420045 0.000470811
## 509 0 Drug 4 2 -0.193191921 0.000470811
## 510 0 Drug 5 2 1.438168677 0.000470811
## 511 0 Placebo 1 3 -0.491868653 0.530776557
## 512 1 Placebo 2 3 1.522743782 0.530776557
## 513 2 Placebo 3 3 0.426522824 0.530776557
## 514 3 Placebo 4 3 0.272620999 0.530776557
## 515 4 Placebo 5 3 2.351441857 0.530776557
## 516 0 Drug 1 4 -0.300571481 -0.299134848
## 517 0 Drug 2 4 -1.113603250 -0.299134848
## 518 0 Drug 3 4 -1.193058425 -0.299134848
## 519 0 Drug 4 4 1.584148130 -0.299134848
## 520 0 Drug 5 4 0.770379085 -0.299134848
## 521 0 Drug 1 5 1.364537023 -0.291373781
## 522 0 Drug 2 5 0.311156344 -0.291373781
## 523 0 Drug 3 5 -0.234832865 -0.291373781
## 524 0 Drug 4 5 -0.221053305 -0.291373781
## 525 0 Drug 5 5 0.258916096 -0.291373781
## 526 0 Placebo 1 6 -0.301872061 -0.155816514
## 527 1 Placebo 2 6 0.968753879 -0.155816514
## 528 2 Placebo 3 6 -1.093843905 -0.155816514
## 529 3 Placebo 4 6 -1.173324609 -0.155816514
## 530 4 Placebo 5 6 0.258466150 -0.155816514
## 531 0 Placebo 1 7 1.577591578 -0.582742608
## 532 1 Placebo 2 7 1.003464136 -0.582742608
## 533 2 Placebo 3 7 -0.900336218 -0.582742608
## 534 3 Placebo 4 7 -1.712261998 -0.582742608
## 535 4 Placebo 5 7 1.736500500 -0.582742608
## 536 0 Placebo 1 8 -0.204912423 -0.033290857
## 537 1 Placebo 2 8 -0.967011597 -0.033290857
## 538 2 Placebo 3 8 -0.076150365 -0.033290857
## 539 3 Placebo 4 8 -1.133984051 -0.033290857
## 540 4 Placebo 5 8 -0.498273892 -0.033290857
## 541 0 Drug 1 9 0.096187555 -0.202399471
## 542 0 Drug 2 9 -0.567981505 -0.202399471
## 543 0 Drug 3 9 0.782119434 -0.202399471
## 544 0 Drug 4 9 1.120806551 -0.202399471
## 545 0 Drug 5 9 2.264973420 -0.202399471
## 546 0 Placebo 1 10 -0.637512323 0.399168691
## 547 1 Placebo 2 10 0.644568860 0.399168691
## 548 2 Placebo 3 10 -1.745014850 0.399168691
## 549 3 Placebo 4 10 1.618267736 0.399168691
## 550 4 Placebo 5 10 0.182168521 0.399168691
## 551 0 Placebo 1 11 -1.524113514 0.876883692
## 552 1 Placebo 2 11 -0.162525621 0.876883692
## 553 2 Placebo 3 11 -0.157676737 0.876883692
## 554 3 Placebo 4 11 0.737738536 0.876883692
## 555 4 Placebo 5 11 1.044825131 0.876883692
## 556 0 Drug 1 12 -1.088196537 0.485723070
## 557 0 Drug 2 12 0.170995555 0.485723070
## 558 0 Drug 3 12 -0.917856560 0.485723070
## 559 0 Drug 4 12 -0.642172114 0.485723070
## 560 0 Drug 5 12 0.163028353 0.485723070
## 561 0 Drug 1 13 -0.111297150 -0.402416281
## 562 0 Drug 2 13 -1.036462201 -0.402416281
## 563 0 Drug 3 13 0.143547233 -0.402416281
## 564 0 Drug 4 13 1.073913019 -0.402416281
## 565 0 Drug 5 13 0.629384137 -0.402416281
## 566 0 Drug 1 14 -0.502120204 -1.013854089
## 567 0 Drug 2 14 -0.362474563 -1.013854089
## 568 0 Drug 3 14 0.729388138 -1.013854089
## 569 0 Drug 4 14 -0.242741408 -1.013854089
## 570 0 Drug 5 14 0.280074127 -1.013854089
## 571 0 Placebo 1 15 0.808448019 -0.240328245
## 572 1 Placebo 2 15 -1.211790890 -0.240328245
## 573 2 Placebo 3 15 0.804151137 -0.240328245
## 574 3 Placebo 4 15 -0.885278968 -0.240328245
## 575 4 Placebo 5 15 -0.611173371 -0.240328245
## 576 0 Placebo 1 16 -1.834427383 0.068287656
## 577 1 Placebo 2 16 -0.261060199 0.068287656
## 578 2 Placebo 3 16 0.686322471 0.068287656
## 579 3 Placebo 4 16 -0.129070226 0.068287656
## 580 4 Placebo 5 16 -0.598654700 0.068287656
## 581 0 Placebo 1 17 -1.973934606 -0.034395495
## 582 1 Placebo 2 17 0.581482445 -0.034395495
## 583 2 Placebo 3 17 -0.891184635 -0.034395495
## 584 3 Placebo 4 17 0.709684665 -0.034395495
## 585 4 Placebo 5 17 -0.281521456 -0.034395495
## 586 0 Drug 1 18 -0.256232203 0.119549174
## 587 0 Drug 2 18 -0.201855727 0.119549174
## 588 0 Drug 3 18 0.047024244 0.119549174
## 589 0 Drug 4 18 1.564363220 0.119549174
## 590 0 Drug 5 18 2.154851976 0.119549174
## 591 0 Drug 1 19 0.804079772 0.112866665
## 592 0 Drug 2 19 1.754158985 0.112866665
## 593 0 Drug 3 19 1.929461716 0.112866665
## 594 0 Drug 4 19 -0.207499406 0.112866665
## 595 0 Drug 5 19 1.265969523 0.112866665
## 596 0 Drug 1 20 0.191436412 -0.560397449
## 597 0 Drug 2 20 -1.896306493 -0.560397449
## 598 0 Drug 3 20 -0.700149229 -0.560397449
## 599 0 Drug 4 20 -2.103309242 -0.560397449
## 600 0 Drug 5 20 -0.519269072 -0.560397449
## 601 0 Placebo 1 21 1.162514865 -0.652535249
## 602 1 Placebo 2 21 0.236627160 -0.652535249
## 603 2 Placebo 3 21 -0.439808745 -0.652535249
## 604 3 Placebo 4 21 0.180180411 -0.652535249
## 605 4 Placebo 5 21 0.060707757 -0.652535249
## 606 0 Placebo 1 22 -1.277323588 0.048290648
## 607 1 Placebo 2 22 -1.081731606 0.048290648
## 608 2 Placebo 3 22 0.233049280 0.048290648
## 609 3 Placebo 4 22 -0.341144189 0.048290648
## 610 4 Placebo 5 22 -1.660233303 0.048290648
## 611 0 Drug 1 23 0.291081621 -0.065488510
## 612 0 Drug 2 23 0.797484996 -0.065488510
## 613 0 Drug 3 23 0.277739448 -0.065488510
## 614 0 Drug 4 23 -1.403582588 -0.065488510
## 615 0 Drug 5 23 0.235703851 -0.065488510
## 616 0 Drug 1 24 -0.238097146 -0.298234774
## 617 0 Drug 2 24 -0.287956781 -0.298234774
## 618 0 Drug 3 24 -0.141261182 -0.298234774
## 619 0 Drug 4 24 -0.321702697 -0.298234774
## 620 0 Drug 5 24 0.196252681 -0.298234774
## 621 0 Placebo 1 25 -0.254293074 0.016024186
## 622 1 Placebo 2 25 0.438800969 0.016024186
## 623 2 Placebo 3 25 -0.359635168 0.016024186
## 624 3 Placebo 4 25 -1.376765043 0.016024186
## 625 4 Placebo 5 25 -0.010167705 0.016024186
## 626 0 Placebo 1 26 0.315012504 0.361820890
## 627 1 Placebo 2 26 -0.418372622 0.361820890
## 628 2 Placebo 3 26 -0.573013176 0.361820890
## 629 3 Placebo 4 26 -0.343817046 0.361820890
## 630 4 Placebo 5 26 0.895614672 0.361820890
## 631 0 Placebo 1 27 -2.729406302 0.663539482
## 632 1 Placebo 2 27 -0.063334709 0.663539482
## 633 2 Placebo 3 27 -1.874601564 0.663539482
## 634 3 Placebo 4 27 0.944900148 0.663539482
## 635 4 Placebo 5 27 0.834063257 0.663539482
## 636 0 Drug 1 28 0.151440137 0.310033159
## 637 0 Drug 2 28 -0.162906233 0.310033159
## 638 0 Drug 3 28 -0.093838776 0.310033159
## 639 0 Drug 4 28 -0.161775262 0.310033159
## 640 0 Drug 5 28 1.769579449 0.310033159
## 641 0 Drug 1 29 0.012002007 0.014488198
## 642 0 Drug 2 29 -0.401234820 0.014488198
## 643 0 Drug 3 29 0.649691496 0.014488198
## 644 0 Drug 4 29 -0.281249881 0.014488198
## 645 0 Drug 5 29 3.204050302 0.014488198
## 646 0 Drug 1 30 1.079987140 0.325145194
## 647 0 Drug 2 30 0.863104700 0.325145194
## 648 0 Drug 3 30 0.581069609 0.325145194
## 649 0 Drug 4 30 -0.141316552 0.325145194
## 650 0 Drug 5 30 -1.134080314 0.325145194
## 651 0 Placebo 1 31 1.338825893 -0.408991827
## 652 1 Placebo 2 31 -1.656604502 -0.408991827
## 653 2 Placebo 3 31 1.738417905 -0.408991827
## 654 3 Placebo 4 31 -0.167309972 -0.408991827
## 655 4 Placebo 5 31 -1.099088636 -0.408991827
## 656 0 Drug 1 32 1.018306348 -0.522951993
## 657 0 Drug 2 32 -1.308215924 -0.522951993
## 658 0 Drug 3 32 1.934545422 -0.522951993
## 659 0 Drug 4 32 -0.361440732 -0.522951993
## 660 0 Drug 5 32 -0.486590021 -0.522951993
## 661 0 Drug 1 33 -1.445593785 0.251928085
## 662 0 Drug 2 33 0.363604830 0.251928085
## 663 0 Drug 3 33 -0.674175177 0.251928085
## 664 0 Drug 4 33 0.261611243 0.251928085
## 665 0 Drug 5 33 -1.046970090 0.251928085
## 666 0 Placebo 1 34 1.871096410 0.285648603
## 667 1 Placebo 2 34 0.269386926 0.285648603
## 668 2 Placebo 3 34 0.099946979 0.285648603
## 669 3 Placebo 4 34 2.555085497 0.285648603
## 670 4 Placebo 5 34 -0.149397592 0.285648603
## 671 0 Drug 1 35 0.013785428 -0.133476270
## 672 0 Drug 2 35 0.615441867 -0.133476270
## 673 0 Drug 3 35 -0.580306520 -0.133476270
## 674 0 Drug 4 35 0.831334711 -0.133476270
## 675 0 Drug 5 35 1.911555067 -0.133476270
## 676 0 Drug 1 36 -0.731209572 0.082337809
## 677 0 Drug 2 36 -0.327661230 0.082337809
## 678 0 Drug 3 36 -0.611672981 0.082337809
## 679 0 Drug 4 36 1.597279437 0.082337809
## 680 0 Drug 5 36 -0.276397256 0.082337809
## 681 0 Placebo 1 37 -0.456397111 0.571848112
## 682 1 Placebo 2 37 2.387648301 0.571848112
## 683 2 Placebo 3 37 0.148913420 0.571848112
## 684 3 Placebo 4 37 0.794277465 0.571848112
## 685 4 Placebo 5 37 0.622545445 0.571848112
## 686 0 Drug 1 38 1.047726878 0.479377839
## 687 0 Drug 2 38 1.238653469 0.479377839
## 688 0 Drug 3 38 0.873104045 0.479377839
## 689 0 Drug 4 38 0.662954158 0.479377839
## 690 0 Drug 5 38 -1.219229361 0.479377839
## 691 0 Drug 1 39 1.203939608 0.180797965
## 692 0 Drug 2 39 -1.777589252 0.180797965
## 693 0 Drug 3 39 -0.948541538 0.180797965
## 694 0 Drug 4 39 0.042758764 0.180797965
## 695 0 Drug 5 39 1.256357503 0.180797965
## 696 0 Placebo 1 40 -0.479984917 0.329898440
## 697 1 Placebo 2 40 1.264162631 0.329898440
## 698 2 Placebo 3 40 -0.745050595 0.329898440
## 699 3 Placebo 4 40 0.013618457 0.329898440
## 700 4 Placebo 5 40 -0.326840673 0.329898440
## 701 0 Drug 1 41 -1.825700030 0.035732339
## 702 0 Drug 2 41 -0.619163701 0.035732339
## 703 0 Drug 3 41 0.214006914 0.035732339
## 704 0 Drug 4 41 -0.155997580 0.035732339
## 705 0 Drug 5 41 1.438773613 0.035732339
## 706 0 Placebo 1 42 0.229507837 -0.324829170
## 707 1 Placebo 2 42 -0.140075584 -0.324829170
## 708 2 Placebo 3 42 1.034297752 -0.324829170
## 709 3 Placebo 4 42 0.356995063 -0.324829170
## 710 4 Placebo 5 42 -0.229241945 -0.324829170
## 711 0 Drug 1 43 0.444347648 0.036928712
## 712 0 Drug 2 43 2.364476200 0.036928712
## 713 0 Drug 3 43 -0.615171130 0.036928712
## 714 0 Drug 4 43 -0.406385657 0.036928712
## 715 0 Drug 5 43 0.271418815 0.036928712
## 716 0 Placebo 1 44 -0.561770431 0.675115986
## 717 1 Placebo 2 44 -0.490709368 0.675115986
## 718 2 Placebo 3 44 0.511349732 0.675115986
## 719 3 Placebo 4 44 -1.016079424 0.675115986
## 720 4 Placebo 5 44 0.204581149 0.675115986
## 721 0 Drug 1 45 -1.245446356 -0.006369750
## 722 0 Drug 2 45 -1.520050943 -0.006369750
## 723 0 Drug 3 45 -0.246084698 -0.006369750
## 724 0 Drug 4 45 0.114987153 -0.006369750
## 725 0 Drug 5 45 1.293461102 -0.006369750
## 726 0 Placebo 1 46 -0.157388527 0.614784453
## 727 1 Placebo 2 46 -0.185644913 0.614784453
## 728 2 Placebo 3 46 -0.372705258 0.614784453
## 729 3 Placebo 4 46 -0.781234545 0.614784453
## 730 4 Placebo 5 46 -0.481369057 0.614784453
## 731 0 Placebo 1 47 0.364944036 -0.193538348
## 732 1 Placebo 2 47 -1.001304890 -0.193538348
## 733 2 Placebo 3 47 0.037134303 -0.193538348
## 734 3 Placebo 4 47 -0.737698644 -0.193538348
## 735 4 Placebo 5 47 -1.226995959 -0.193538348
## 736 0 Placebo 1 48 0.541777143 0.761056050
## 737 1 Placebo 2 48 0.269651025 0.761056050
## 738 2 Placebo 3 48 0.733426490 0.761056050
## 739 3 Placebo 4 48 0.709884537 0.761056050
## 740 4 Placebo 5 48 -0.345595060 0.761056050
## 741 0 Placebo 1 49 -1.223940872 -0.094371442
## 742 1 Placebo 2 49 -0.888566695 -0.094371442
## 743 2 Placebo 3 49 1.742661947 -0.094371442
## 744 3 Placebo 4 49 0.124440114 -0.094371442
## 745 4 Placebo 5 49 0.898985807 -0.094371442
## 746 0 Drug 1 50 0.876479648 0.257546347
## 747 0 Drug 2 50 -0.191437925 0.257546347
## 748 0 Drug 3 50 -0.293098479 0.257546347
## 749 0 Drug 4 50 -0.238169962 0.257546347
## 750 0 Drug 5 50 -0.837985795 0.257546347
## time_clust fixed_outcome random_effects resp_var
## 1 -0.434792035 4.75 -0.1333095715 3.6187407
## 2 -0.434792035 5.25 -0.5681016065 2.4262835
## 3 -0.434792035 5.75 -1.0028936415 4.8580658
## 4 -0.434792035 6.25 -1.4376856766 4.5314398
## 5 -0.434792035 6.75 -1.8724777116 4.9163314
## 6 -0.037096003 4.75 -0.4808816845 2.6748428
## 7 -0.037096003 5.25 -0.5179776880 4.9447094
## 8 -0.037096003 5.75 -0.5550736914 4.4223218
## 9 -0.037096003 6.25 -0.5921696949 4.6509906
## 10 -0.037096003 6.75 -0.6292656984 7.0307710
## 11 -0.817042951 4.75 -0.7360204695 2.3516298
## 12 -0.817042951 5.25 -1.5530634206 2.2524937
## 13 -0.817042951 5.75 -2.3701063717 3.3120818
## 14 -0.817042951 6.25 -3.1871493228 3.8544309
## 15 -0.817042951 6.75 -4.0041922739 2.9951603
## 16 0.707211722 4.75 0.1432624191 4.3529251
## 17 0.707211722 5.25 0.8504741410 5.9249878
## 18 0.707211722 5.75 1.5576858629 7.6863140
## 19 0.707211722 6.25 2.2648975848 8.9144764
## 20 0.707211722 6.75 2.9721093067 10.1705187
## 21 -0.174110167 4.00 -0.1601758024 2.6861142
## 22 -0.174110167 4.50 -0.3342859698 3.6028428
## 23 -0.174110167 5.00 -0.5083961373 3.4652462
## 24 -0.174110167 5.50 -0.6825063047 4.7703839
## 25 -0.174110167 6.00 -0.8566164722 4.7933263
## 26 -0.136912555 4.00 -0.4433900135 3.4019408
## 27 -0.136912555 4.50 -0.5803025680 3.1537024
## 28 -0.136912555 5.00 -0.7172151226 4.3752692
## 29 -0.136912555 5.50 -0.8541276771 3.6805770
## 30 -0.136912555 6.00 -0.9910402317 3.9251269
## 31 0.033224804 4.00 0.0362174867 3.5212460
## 32 0.033224804 4.50 0.0694422905 4.4314060
## 33 0.033224804 5.00 0.1026670943 3.3754730
## 34 0.033224804 5.50 0.1358918981 3.7611504
## 35 0.033224804 6.00 0.1691167019 6.0860115
## 36 -0.149375584 4.00 -0.3459476330 3.0763967
## 37 -0.149375584 4.50 -0.4953232170 3.7533153
## 38 -0.149375584 5.00 -0.6446988009 4.8451810
## 39 -0.149375584 5.50 -0.7940743848 4.2535793
## 40 -0.149375584 6.00 -0.9434499688 5.0345288
## 41 -0.074476752 4.75 -0.0789050653 5.6666537
## 42 -0.074476752 5.25 -0.1533818171 4.8037247
## 43 -0.074476752 5.75 -0.2278585688 5.2736880
## 44 -0.074476752 6.25 -0.3023353205 5.7538720
## 45 -0.074476752 6.75 -0.3768120723 5.2522226
## 46 0.257621663 4.00 0.0533713430 4.0856556
## 47 0.257621663 4.50 0.3109930061 4.7205625
## 48 0.257621663 5.00 0.5686146692 5.2802141
## 49 0.257621663 5.50 0.8262363322 5.3997528
## 50 0.257621663 6.00 1.0838579953 7.9481976
## 51 0.154376702 4.75 0.3620510273 6.1367337
## 52 0.154376702 5.25 0.5164277297 5.5638087
## 53 0.154376702 5.75 0.6708044320 6.7085174
## 54 0.154376702 6.25 0.8251811343 8.1581695
## 55 0.154376702 6.75 0.9795578366 6.9504620
## 56 0.416612004 4.00 0.2297620133 4.0213420
## 57 0.416612004 4.50 0.6463740171 4.9768828
## 58 0.416612004 5.00 1.0629860208 6.6340980
## 59 0.416612004 5.50 1.4795980246 7.0488892
## 60 0.416612004 6.00 1.8962100283 8.3752718
## 61 -0.037780769 4.75 -0.1318710719 2.7095619
## 62 -0.037780769 5.25 -0.1696518405 6.3152511
## 63 -0.037780769 5.75 -0.2074326092 6.4941007
## 64 -0.037780769 6.25 -0.2452133778 5.6331749
## 65 -0.037780769 6.75 -0.2829941464 6.9969853
## 66 0.459038919 4.00 0.0115472182 4.1174087
## 67 0.459038919 4.50 0.4705861370 5.1061075
## 68 0.459038919 5.00 0.9296250559 6.9009810
## 69 0.459038919 5.50 1.3886639747 7.4125544
## 70 0.459038919 6.00 1.8477028936 9.3572892
## 71 -0.565311663 4.00 0.0448689338 4.3203425
## 72 -0.565311663 4.50 -0.5204427296 2.8677578
## 73 -0.565311663 5.00 -1.0857543930 4.7591390
## 74 -0.565311663 5.50 -1.6510660564 3.7863123
## 75 -0.565311663 6.00 -2.2163777198 3.8295538
## 76 0.936134637 4.00 0.6355967162 4.9380828
## 77 0.936134637 4.50 1.5717313535 6.3098712
## 78 0.936134637 5.00 2.5078659908 5.9612349
## 79 0.936134637 5.50 3.4440006280 8.7680421
## 80 0.936134637 6.00 4.3801352653 11.5195687
## 81 -0.038677920 4.00 0.5097757500 4.9227468
## 82 -0.038677920 4.50 0.4710978303 4.2304659
## 83 -0.038677920 5.00 0.4324199107 6.7176809
## 84 -0.038677920 5.50 0.3937419910 3.3569490
## 85 -0.038677920 6.00 0.3550640714 6.0713163
## 86 0.051883366 4.75 0.2474623635 5.4326641
## 87 0.051883366 5.25 0.2993457298 5.8506624
## 88 0.051883366 5.75 0.3512290962 5.1857755
## 89 0.051883366 6.25 0.4031124625 4.8777709
## 90 0.051883366 6.75 0.4549958289 9.9781377
## 91 0.411680986 4.75 0.2823895520 4.9660184
## 92 0.411680986 5.25 0.6940705385 4.8386563
## 93 0.411680986 5.75 1.1057515249 6.0845645
## 94 0.411680986 6.25 1.5174325114 8.7790515
## 95 0.411680986 6.75 1.9291134978 9.4966930
## 96 0.150236660 4.75 1.0858594270 6.4953708
## 97 0.150236660 5.25 1.2360960867 6.5081028
## 98 0.150236660 5.75 1.3863327465 7.6950090
## 99 0.150236660 6.25 1.5365694063 5.7711202
## 100 0.150236660 6.75 1.6868060661 7.6115794
## 101 -0.127830129 4.00 0.7462364131 6.3685515
## 102 -0.127830129 4.50 0.6184062838 4.5962798
## 103 -0.127830129 5.00 0.4905761546 4.3851050
## 104 -0.127830129 5.50 0.3627460253 5.6618162
## 105 -0.127830129 6.00 0.2349158960 6.3084406
## 106 0.753189123 4.75 -0.3018501343 4.3504398
## 107 0.753189123 5.25 0.4513389886 5.9452771
## 108 0.753189123 5.75 1.2045281116 6.8534810
## 109 0.753189123 6.25 1.9577172346 9.5966556
## 110 0.753189123 6.75 2.7109063576 7.4883235
## 111 -0.668759530 4.00 0.0272500192 5.3868884
## 112 -0.668759530 4.50 -0.6415095105 4.1207100
## 113 -0.668759530 5.00 -1.3102690402 3.0927596
## 114 -0.668759530 5.50 -1.9790285699 3.1628189
## 115 -0.668759530 6.00 -2.6477880996 1.9972382
## 116 -0.886540754 4.00 0.1930991830 4.4558133
## 117 -0.886540754 4.50 -0.6934415712 3.5306711
## 118 -0.886540754 5.00 -1.5799823253 3.1700974
## 119 -0.886540754 5.50 -2.4665230795 3.1951419
## 120 -0.886540754 6.00 -3.3530638337 2.0465156
## 121 0.567112942 4.75 0.4231611497 5.8140100
## 122 0.567112942 5.25 0.9902740916 5.6550376
## 123 0.567112942 5.75 1.5573870335 8.0867511
## 124 0.567112942 6.25 2.1244999754 7.7225384
## 125 0.567112942 6.75 2.6916129173 10.1648091
## 126 -0.009350836 4.00 0.8495090321 6.1386270
## 127 -0.009350836 4.50 0.8401581962 6.6019980
## 128 -0.009350836 5.00 0.8308073603 4.7011324
## 129 -0.009350836 5.50 0.8214565244 5.8125808
## 130 -0.009350836 6.00 0.8121056885 6.4720387
## 131 -0.070036358 4.00 0.2122637705 2.6817976
## 132 -0.070036358 4.50 0.1422274120 7.5096072
## 133 -0.070036358 5.00 0.0721910536 4.9004121
## 134 -0.070036358 5.50 0.0021546951 6.6915709
## 135 -0.070036358 6.00 -0.0678816633 6.1269475
## 136 -1.111416839 4.00 -0.8396267056 4.2003699
## 137 -1.111416839 4.50 -1.9510435443 0.7237975
## 138 -1.111416839 5.00 -3.0624603831 2.1682675
## 139 -1.111416839 5.50 -4.1738772219 1.7185827
## 140 -1.111416839 6.00 -5.2852940606 0.7166593
## 141 -0.665330582 4.75 0.3878081306 4.2394032
## 142 -0.665330582 5.25 -0.2775224515 3.1315515
## 143 -0.665330582 5.75 -0.9428530337 5.9175857
## 144 -0.665330582 6.25 -1.6081836158 3.2457043
## 145 -0.665330582 6.75 -2.2735141979 4.6489277
## 146 0.089565113 4.75 0.0632632012 4.0677242
## 147 0.089565113 5.25 0.1528283145 5.1258934
## 148 0.089565113 5.75 0.2423934278 6.2390661
## 149 0.089565113 6.25 0.3319585411 6.2582348
## 150 0.089565113 6.75 0.4215236544 7.5782756
## 151 -0.249634322 4.75 -0.3642926814 4.2245285
## 152 -0.249634322 5.25 -0.6139270037 3.2168154
## 153 -0.249634322 5.75 -0.8635613260 5.5162767
## 154 -0.249634322 6.25 -1.1131956483 4.3019269
## 155 -0.249634322 6.75 -1.3628299706 5.7987323
## 156 0.033908361 4.00 0.7300696364 5.5280055
## 157 0.033908361 4.50 0.7639779974 5.1149665
## 158 0.033908361 5.00 0.7978863584 4.9838099
## 159 0.033908361 5.50 0.8317947194 7.6714060
## 160 0.033908361 6.00 0.8657030804 5.1631800
## 161 0.404331805 4.75 -1.3212359821 2.6577958
## 162 0.404331805 5.25 -0.9169041774 6.3736198
## 163 0.404331805 5.75 -0.5125723727 5.7854975
## 164 0.404331805 6.25 -0.1082405679 6.7631215
## 165 0.404331805 6.75 0.2960912368 6.0384304
## 166 0.324300404 4.75 0.0140146352 6.1007270
## 167 0.324300404 5.25 0.3383150396 7.0241632
## 168 0.324300404 5.75 0.6626154440 6.6654688
## 169 0.324300404 6.25 0.9869158484 5.9357463
## 170 0.324300404 6.75 1.3112162527 8.3995213
## 171 -0.134403158 4.00 -0.3845789966 3.6500726
## 172 -0.134403158 4.50 -0.5189821543 4.4009489
## 173 -0.134403158 5.00 -0.6533853120 3.0702578
## 174 -0.134403158 5.50 -0.7877884697 2.8608443
## 175 -0.134403158 6.00 -0.9221916274 5.1615348
## 176 0.501313994 4.75 -0.2522531302 5.9726916
## 177 0.501313994 5.25 0.2490608634 5.4350856
## 178 0.501313994 5.75 0.7503748571 5.7904507
## 179 0.501313994 6.25 1.2516888508 6.7270286
## 180 0.501313994 6.75 1.7530028444 10.1611282
## 181 -0.394218534 4.00 -0.2603498023 4.9060108
## 182 -0.394218534 4.50 -0.6545683359 6.6513061
## 183 -0.394218534 5.00 -1.0487868696 4.4289300
## 184 -0.394218534 5.50 -1.4430054032 4.6226191
## 185 -0.394218534 6.00 -1.8372239368 4.1879981
## 186 1.134752376 4.75 -0.3476382332 3.9928145
## 187 1.134752376 5.25 0.7871141431 6.1048630
## 188 1.134752376 5.75 1.9218665193 7.3414545
## 189 1.134752376 6.25 3.0566188955 9.5660714
## 190 1.134752376 6.75 4.1913712718 11.1155026
## 191 0.210203592 4.75 -0.0693383744 5.4962608
## 192 0.210203592 5.25 0.1408652177 4.3406272
## 193 0.210203592 5.75 0.3510688097 6.4679046
## 194 0.210203592 6.25 0.5612724018 5.9889786
## 195 0.210203592 6.75 0.7714759939 9.6379295
## 196 0.774625088 4.00 0.5950545261 5.1032366
## 197 0.774625088 4.50 1.3696796143 6.4250236
## 198 0.774625088 5.00 2.1443047024 6.4002770
## 199 0.774625088 5.50 2.9189297906 8.8289376
## 200 0.774625088 6.00 3.6935548787 9.8492188
## 201 0.056604575 4.00 0.0614693180 4.3045140
## 202 0.056604575 4.50 0.1180738934 5.2425431
## 203 0.056604575 5.00 0.1746784687 4.5379958
## 204 0.056604575 5.50 0.2312830440 4.7979680
## 205 0.056604575 6.00 0.2878876194 5.6086179
## 206 -0.942636456 4.00 0.1040915892 3.9438418
## 207 -0.942636456 4.50 -0.8385448667 3.2120277
## 208 -0.942636456 5.00 -1.7811813226 2.1673153
## 209 -0.942636456 5.50 -2.7238177784 3.3626348
## 210 -0.942636456 6.00 -3.6664542343 3.8362004
## 211 0.153709795 4.75 -0.2583082761 5.4898282
## 212 0.153709795 5.25 -0.1045984810 5.7737541
## 213 0.153709795 5.75 0.0491113141 5.9445764
## 214 0.153709795 6.25 0.2028211093 6.0122044
## 215 0.153709795 6.75 0.3565309044 6.5704960
## 216 -0.861799289 4.00 0.6913050339 4.5164392
## 217 -0.861799289 4.50 -0.1704942554 5.3113567
## 218 -0.861799289 5.00 -1.0322935446 1.9140731
## 219 -0.861799289 5.50 -1.8940928339 4.2992505
## 220 -0.861799289 6.00 -2.7558921232 4.0777168
## 221 0.299435562 4.75 -0.2856422852 4.9526279
## 222 0.299435562 5.25 0.0137932767 3.3851169
## 223 0.299435562 5.75 0.3132288385 6.0310973
## 224 0.299435562 6.25 0.6126644003 8.2780043
## 225 0.299435562 6.75 0.9120999621 7.6795982
## 226 0.774304778 4.00 0.6638972778 5.7184200
## 227 0.774304778 4.50 1.4382020560 6.4834094
## 228 0.774304778 5.00 2.2125068342 8.2928483
## 229 0.774304778 5.50 2.9868116124 6.5215695
## 230 0.774304778 6.00 3.7611163906 9.2864478
## 231 -0.356488109 4.75 0.0083182491 6.1663583
## 232 -0.356488109 5.25 -0.3481698601 4.0948555
## 233 -0.356488109 5.75 -0.7046579693 3.8400701
## 234 -0.356488109 6.25 -1.0611460784 3.4298706
## 235 -0.356488109 6.75 -1.4176341876 5.1986710
## 236 -0.868411466 4.00 0.6887832877 6.1632773
## 237 -0.868411466 4.50 -0.1796281782 6.3363999
## 238 -0.868411466 5.00 -1.0480396440 4.0498311
## 239 -0.868411466 5.50 -1.9164511099 5.3245108
## 240 -0.868411466 6.00 -2.7848625758 2.1390846
## 241 -0.169105067 4.75 0.3200571656 6.4284598
## 242 -0.169105067 5.25 0.1509520986 6.2522450
## 243 -0.169105067 5.75 -0.0181529685 5.8007830
## 244 -0.169105067 6.25 -0.1872580356 6.2025736
## 245 -0.169105067 6.75 -0.3563631026 5.5754451
## 246 0.111334669 4.75 0.0080983778 5.8105295
## 247 0.111334669 5.25 0.1194330466 5.1925771
## 248 0.111334669 5.75 0.2307677154 6.9234364
## 249 0.111334669 6.25 0.3421023841 5.8168938
## 250 0.111334669 6.75 0.4534370529 6.1719346
## 251 0.145480807 4.75 -0.5652346847 2.8947339
## 252 0.145480807 5.25 -0.4197538774 5.1157388
## 253 0.145480807 5.75 -0.2742730700 4.2216436
## 254 0.145480807 6.25 -0.1287922627 6.3531070
## 255 0.145480807 6.75 0.0166885446 8.0860434
## 256 1.036424979 4.00 -0.3719967869 4.3020969
## 257 1.036424979 4.50 0.6644281919 5.1882435
## 258 1.036424979 5.00 1.7008531708 8.0615392
## 259 1.036424979 5.50 2.7372781497 7.3015132
## 260 1.036424979 6.00 3.7737031286 12.1027443
## 261 0.331574848 4.75 -0.4584789218 4.6321240
## 262 0.331574848 5.25 -0.1269040739 5.8482733
## 263 0.331574848 5.75 0.2046707739 4.9937883
## 264 0.331574848 6.25 0.5362456218 5.5791173
## 265 0.331574848 6.75 0.8678204697 7.8899661
## 266 -0.326763892 4.00 0.0352068436 4.3931387
## 267 -0.326763892 4.50 -0.2915570488 3.0142322
## 268 -0.326763892 5.00 -0.6183209412 4.3248915
## 269 -0.326763892 5.50 -0.9450848335 5.2859075
## 270 -0.326763892 6.00 -1.2718487259 4.6788363
## 271 0.176309464 4.00 -0.2646797874 3.0736116
## 272 0.176309464 4.50 -0.0883703232 5.2964849
## 273 0.176309464 5.00 0.0879391410 3.9874325
## 274 0.176309464 5.50 0.2642486052 5.1187815
## 275 0.176309464 6.00 0.4405580695 7.6142533
## 276 -0.091807529 4.75 0.1466506848 5.3671962
## 277 -0.091807529 5.25 0.0548431559 5.0993894
## 278 -0.091807529 5.75 -0.0369643730 6.1198883
## 279 -0.091807529 6.25 -0.1287719019 6.1167549
## 280 -0.091807529 6.75 -0.2205794308 7.2343953
## 281 -0.488467789 4.75 0.6925174045 6.1166803
## 282 -0.488467789 5.25 0.2040496155 5.6559304
## 283 -0.488467789 5.75 -0.2844181735 6.2198769
## 284 -0.488467789 6.25 -0.7728859626 4.7863820
## 285 -0.488467789 6.75 -1.2613537516 4.7929114
## 286 -0.690236997 4.00 -0.3053470745 4.0738734
## 287 -0.690236997 4.50 -0.9955840714 3.6519576
## 288 -0.690236997 5.00 -1.6858210684 2.7811222
## 289 -0.690236997 5.50 -2.3760580653 2.4835497
## 290 -0.690236997 6.00 -3.0662950623 2.5288184
## 291 0.491243359 4.75 0.7689083038 4.2471900
## 292 0.491243359 5.25 1.2601516631 6.0508572
## 293 0.491243359 5.75 1.7513950224 6.1856521
## 294 0.491243359 6.25 2.2426383817 9.5772423
## 295 0.491243359 6.75 2.7338817410 10.6793476
## 296 0.237832097 4.00 -0.1903014840 2.3238829
## 297 0.237832097 4.50 0.0475306128 5.3926346
## 298 0.237832097 5.00 0.2853627096 7.8598253
## 299 0.237832097 5.50 0.5231948065 5.6286261
## 300 0.237832097 6.00 0.7610269033 6.3483170
## 301 -0.608651317 4.75 0.2966377059 4.8310913
## 302 -0.608651317 5.25 -0.3120136115 4.3819408
## 303 -0.608651317 5.75 -0.9206649288 5.6484544
## 304 -0.608651317 6.25 -1.5293162461 4.0720932
## 305 -0.608651317 6.75 -2.1379675635 5.6900692
## 306 0.061895505 4.75 0.4169432114 4.5625091
## 307 0.061895505 5.25 0.4788387167 6.2573734
## 308 0.061895505 5.75 0.5407342220 6.6672719
## 309 0.061895505 6.25 0.6026297272 6.7188100
## 310 0.061895505 6.75 0.6645252325 5.8623012
## 311 0.546555847 4.75 -0.4617015535 3.8214823
## 312 0.546555847 5.25 0.0848542931 5.7160705
## 313 0.546555847 5.75 0.6314101397 6.4434755
## 314 0.546555847 6.25 1.1779659863 7.8361742
## 315 0.546555847 6.75 1.7245218329 9.0381271
## 316 -0.303340153 4.00 0.0115727198 3.8559087
## 317 -0.303340153 4.50 -0.2917674328 4.4478558
## 318 -0.303340153 5.00 -0.5951075854 3.6434843
## 319 -0.303340153 5.50 -0.8984477379 4.8759254
## 320 -0.303340153 6.00 -1.2017878905 6.3093618
## 321 -0.234350796 4.00 -0.1458858953 3.4695306
## 322 -0.234350796 4.50 -0.3802366912 2.6628807
## 323 -0.234350796 5.00 -0.6145874871 3.4984059
## 324 -0.234350796 5.50 -0.8489382830 4.0815922
## 325 -0.234350796 6.00 -1.0832890789 6.2868586
## 326 0.607901710 4.00 0.0813517467 4.3222640
## 327 0.607901710 4.50 0.6892534564 5.0291626
## 328 0.607901710 5.00 1.2971551661 6.2902230
## 329 0.607901710 5.50 1.9050568759 6.7835938
## 330 0.607901710 6.00 2.5129585856 7.9234731
## 331 0.183676677 4.75 -0.4224446586 5.6347357
## 332 0.183676677 5.25 -0.2387679817 6.3592460
## 333 0.183676677 5.75 -0.0550913049 5.9335013
## 334 0.183676677 6.25 0.1285853720 6.1532611
## 335 0.183676677 6.75 0.3122620488 8.2531691
## 336 0.339386592 4.75 -0.0081552621 3.6543913
## 337 0.339386592 5.25 0.3312313299 5.2821758
## 338 0.339386592 5.75 0.6706179218 5.6801424
## 339 0.339386592 6.25 1.0100045137 6.6299597
## 340 0.339386592 6.75 1.3493911056 8.9560455
## 341 -0.513748769 4.00 0.1946988859 3.2126029
## 342 -0.513748769 4.50 -0.3190498836 4.0277461
## 343 -0.513748769 5.00 -0.8327986531 4.0698432
## 344 -0.513748769 5.50 -1.3465474225 4.4579116
## 345 -0.513748769 6.00 -1.8602961920 3.5557764
## 346 -0.060944988 4.75 -0.2341071001 4.9499803
## 347 -0.060944988 5.25 -0.2950520886 4.1938658
## 348 -0.060944988 5.75 -0.3559970770 5.5632835
## 349 -0.060944988 6.25 -0.4169420655 6.1640453
## 350 -0.060944988 6.75 -0.4778870540 5.8723568
## 351 0.014787930 4.00 0.2159446894 4.8291109
## 352 0.014787930 4.50 0.2307326192 5.8854605
## 353 0.014787930 5.00 0.2455205489 7.2624614
## 354 0.014787930 5.50 0.2603084787 5.9373289
## 355 0.014787930 6.00 0.2750964084 5.6550574
## 356 -0.061732357 4.00 0.0755192968 4.7294323
## 357 -0.061732357 4.50 0.0137869400 5.6906379
## 358 -0.061732357 5.00 -0.0479454168 4.4346777
## 359 -0.061732357 5.50 -0.1096777736 6.1083224
## 360 -0.061732357 6.00 -0.1714101304 6.5455731
## 361 -0.707275745 4.75 -0.2112764959 4.8503787
## 362 -0.707275745 5.25 -0.9185522411 5.5921065
## 363 -0.707275745 5.75 -1.6258279863 4.7171415
## 364 -0.707275745 6.25 -2.3331037316 3.0930471
## 365 -0.707275745 6.75 -3.0403794768 2.9454572
## 366 -0.314873034 4.00 -0.7649200523 3.6086934
## 367 -0.314873034 4.50 -1.0797930864 2.1525628
## 368 -0.314873034 5.00 -1.3946661204 4.4995066
## 369 -0.314873034 5.50 -1.7095391545 1.4915345
## 370 -0.314873034 6.00 -2.0244121886 3.3606059
## 371 -0.018114251 4.75 -0.7192920381 2.7003187
## 372 -0.018114251 5.25 -0.7374062892 4.6384259
## 373 -0.018114251 5.75 -0.7555205404 6.0178075
## 374 -0.018114251 6.25 -0.7736347916 5.7117120
## 375 -0.018114251 6.75 -0.7917490427 6.1970834
## 376 0.132395895 4.00 -0.0332385843 3.3228729
## 377 0.132395895 4.50 0.0991573107 5.5654246
## 378 0.132395895 5.00 0.2315532057 4.9917894
## 379 0.132395895 5.50 0.3639491007 5.7589453
## 380 0.132395895 6.00 0.4963449957 5.6322451
## 381 0.338032405 4.75 0.4870783163 4.3212576
## 382 0.338032405 5.25 0.8251107212 6.1239433
## 383 0.338032405 5.75 1.1631431261 8.6643391
## 384 0.338032405 6.25 1.5011755311 7.9244183
## 385 0.338032405 6.75 1.8392079360 7.4987195
## 386 -0.320651092 4.00 0.3793379268 3.9276624
## 387 -0.320651092 4.50 0.0586868343 4.9174823
## 388 -0.320651092 5.00 -0.2619642582 6.0649070
## 389 -0.320651092 5.50 -0.5826153506 6.4276480
## 390 -0.320651092 6.00 -0.9032664431 6.2790244
## 391 -0.383447484 4.00 0.1922001370 5.1233253
## 392 -0.383447484 4.50 -0.1912473467 3.4614265
## 393 -0.383447484 5.00 -0.5746948305 3.7187016
## 394 -0.383447484 5.50 -0.9581423143 4.0984697
## 395 -0.383447484 6.00 -1.3415897981 5.9717243
## 396 0.674106927 4.75 -0.3170778192 5.5410888
## 397 0.674106927 5.25 0.3570291073 5.7683613
## 398 0.674106927 5.75 1.0311360338 7.0029280
## 399 0.674106927 6.25 1.7052429603 8.1877353
## 400 0.674106927 6.75 2.3793498869 7.6783483
## 401 0.176774241 4.75 0.6906438721 8.4408079
## 402 0.176774241 5.25 0.8674181130 5.2196384
## 403 0.176774241 5.75 1.0441923539 5.8122142
## 404 0.176774241 6.25 1.2209665948 5.2325739
## 405 0.176774241 6.75 1.3977408357 9.1508352
## 406 0.526050894 4.00 0.5257310103 5.2513303
## 407 0.526050894 4.50 1.0517819044 4.6593546
## 408 0.526050894 5.00 1.5778327985 6.4901842
## 409 0.526050894 5.50 2.1038836926 8.4752013
## 410 0.526050894 6.00 2.6299345866 7.4565605
## 411 0.502854977 4.00 0.3752345040 5.2156452
## 412 0.502854977 4.50 0.8780894808 5.3858849
## 413 0.502854977 5.00 1.3809444575 6.2019061
## 414 0.502854977 5.50 1.8837994343 7.5147603
## 415 0.502854977 6.00 2.3866544110 9.5638520
## 416 -0.573429575 4.00 -0.0210320674 2.4797143
## 417 -0.573429575 4.50 -0.5944616421 4.7138675
## 418 -0.573429575 5.00 -1.1678912167 3.3264188
## 419 -0.573429575 5.50 -1.7413207914 3.7820479
## 420 -0.573429575 6.00 -2.3147503660 2.3668483
## 421 -0.107368576 4.75 0.6911887274 5.3345519
## 422 -0.107368576 5.25 0.5838201517 6.3041449
## 423 -0.107368576 5.75 0.4764515759 5.3052166
## 424 -0.107368576 6.25 0.3690830001 6.0024788
## 425 -0.107368576 6.75 0.2617144243 7.1861899
## 426 0.510682607 4.75 0.1845821551 5.3714776
## 427 0.510682607 5.25 0.6952647619 7.3649204
## 428 0.510682607 5.75 1.2059473687 6.7250163
## 429 0.510682607 6.25 1.7166299755 7.8324104
## 430 0.510682607 6.75 2.2273125823 8.8586187
## 431 0.353875630 4.75 0.8721533073 5.8463577
## 432 0.353875630 5.25 1.2260289374 9.0187571
## 433 0.353875630 5.75 1.5799045676 6.9129439
## 434 0.353875630 6.25 1.9337801977 8.0293811
## 435 0.353875630 6.75 2.2876558278 10.6183645
## 436 -0.183279339 4.00 -0.1636623406 3.0856323
## 437 -0.183279339 4.50 -0.3469416795 4.2623711
## 438 -0.183279339 5.00 -0.5302210184 3.8203036
## 439 -0.183279339 5.50 -0.7135003573 3.6744048
## 440 -0.183279339 6.00 -0.8967796962 5.2217438
## 441 -0.308637130 4.75 0.9700633970 5.6967064
## 442 -0.308637130 5.25 0.6614262668 7.7475643
## 443 -0.308637130 5.75 0.3527891366 6.4361818
## 444 -0.308637130 6.25 0.0441520064 6.0375138
## 445 -0.308637130 6.75 -0.2644851238 6.9439232
## 446 0.151204899 4.75 0.3444256341 5.3829751
## 447 0.151204899 5.25 0.4956305332 5.9106871
## 448 0.151204899 5.75 0.6468354322 5.8892673
## 449 0.151204899 6.25 0.7980403312 6.1813602
## 450 0.151204899 6.75 0.9492452303 9.0958960
## 451 0.617674728 4.75 -0.0845752231 7.0810479
## 452 0.617674728 5.25 0.5330995048 6.0553206
## 453 0.617674728 5.75 1.1507742327 7.2513589
## 454 0.617674728 6.25 1.7684489606 8.4840726
## 455 0.617674728 6.75 2.3861236885 8.7765988
## 456 -0.498670573 4.00 0.5671989039 4.2576103
## 457 -0.498670573 4.50 0.0685283312 3.3090073
## 458 -0.498670573 5.00 -0.4301422415 4.0819766
## 459 -0.498670573 5.50 -0.9288128142 5.2340577
## 460 -0.498670573 6.00 -1.4274833869 3.5818700
## 461 0.146106577 4.00 -0.2736592008 2.9125758
## 462 0.146106577 4.50 -0.1275526242 2.9865919
## 463 0.146106577 5.00 0.0185539524 5.3985535
## 464 0.146106577 5.50 0.1646605290 5.3069582
## 465 0.146106577 6.00 0.3107671056 5.3483057
## 466 -0.251918938 4.00 -0.0747082989 3.0731235
## 467 -0.251918938 4.50 -0.3266272368 4.9601825
## 468 -0.251918938 5.00 -0.5785461748 2.8178242
## 469 -0.251918938 5.50 -0.8304651127 3.7960074
## 470 -0.251918938 6.00 -1.0823840507 5.9750769
## 471 -0.253658562 4.75 0.1392102617 4.6135746
## 472 -0.253658562 5.25 -0.1144483008 6.8855556
## 473 -0.253658562 5.75 -0.3681068632 4.3403341
## 474 -0.253658562 6.25 -0.6217654257 6.1110121
## 475 -0.253658562 6.75 -0.8754239881 4.5563428
## 476 -0.113383098 4.00 0.0268289109 3.6141863
## 477 -0.113383098 4.50 -0.0865541873 4.0645421
## 478 -0.113383098 5.00 -0.1999372854 3.9954117
## 479 -0.113383098 5.50 -0.3133203835 4.8031306
## 480 -0.113383098 6.00 -0.4267034817 6.4445800
## 481 -0.972436814 4.75 -0.6775260419 4.9298423
## 482 -0.972436814 5.25 -1.6499628563 4.2458087
## 483 -0.972436814 5.75 -2.6223996707 3.6456253
## 484 -0.972436814 6.25 -3.5948364851 1.9505476
## 485 -0.972436814 6.75 -4.5672732994 2.7374847
## 486 0.336823165 4.00 0.9134882897 4.2758356
## 487 0.336823165 4.50 1.2503114549 5.0225318
## 488 0.336823165 5.00 1.5871346202 5.9741603
## 489 0.336823165 5.50 1.9239577854 7.7655798
## 490 0.336823165 6.00 2.2607809506 6.5863877
## 491 -0.581117269 4.75 1.2297893147 6.6584269
## 492 -0.581117269 5.25 0.6486720453 6.7928172
## 493 -0.581117269 5.75 0.0675547759 5.7487389
## 494 -0.581117269 6.25 -0.5135624936 4.5593800
## 495 -0.581117269 6.75 -1.0946797630 4.1530367
## 496 -0.438174613 4.75 0.5101551581 5.4085239
## 497 -0.438174613 5.25 0.0719805446 5.3562419
## 498 -0.438174613 5.75 -0.3661940688 6.4861504
## 499 -0.438174613 6.25 -0.8043686823 4.5775708
## 500 -0.438174613 6.75 -1.2425432958 5.7738987
## 501 -0.540851630 4.00 -0.6096671224 2.3670309
## 502 -0.540851630 4.50 -1.1505187527 4.3727999
## 503 -0.540851630 5.00 -1.6913703830 4.3973949
## 504 -0.540851630 5.50 -2.2322220134 3.6664864
## 505 -0.540851630 6.00 -2.7730736437 4.3157062
## 506 0.119229591 4.00 0.0004708110 4.5984662
## 507 0.119229591 4.50 0.1197004021 5.2328967
## 508 0.119229591 5.00 0.2389299933 4.6965099
## 509 0.119229591 5.50 0.3581595845 5.6649677
## 510 0.119229591 6.00 0.4773891757 7.9155579
## 511 0.186643782 4.75 0.5307765567 4.7889079
## 512 0.186643782 5.25 0.7174203387 7.4901641
## 513 0.186643782 5.75 0.9040641207 7.0805869
## 514 0.186643782 6.25 1.0907079027 7.6133289
## 515 0.186643782 6.75 1.2773516847 10.3787935
## 516 0.523382577 4.00 -0.2991348479 3.4002937
## 517 0.523382577 4.50 0.2242477293 3.6106445
## 518 0.523382577 5.00 0.7476303064 4.5545719
## 519 0.523382577 5.50 1.2710128836 8.3551610
## 520 0.523382577 6.00 1.7943954608 8.5647745
## 521 0.129704529 4.00 -0.2913737813 5.0731632
## 522 0.129704529 4.50 -0.1616692525 4.6494871
## 523 0.129704529 5.00 -0.0319647237 4.7332024
## 524 0.129704529 5.50 0.0977398052 5.3766865
## 525 0.129704529 6.00 0.2274443340 6.4863604
## 526 -0.465383870 4.75 -0.1558165141 4.2923114
## 527 -0.465383870 5.25 -0.6212003837 5.5975535
## 528 -0.465383870 5.75 -1.0865842533 3.5695718
## 529 -0.465383870 6.25 -1.5519681228 3.5247073
## 530 -0.465383870 6.75 -2.0173519924 4.9911142
## 531 0.214772809 4.75 -0.5827426084 5.7448490
## 532 0.214772809 5.25 -0.3679697995 5.8854943
## 533 0.214772809 5.75 -0.1531969906 4.6964668
## 534 0.214772809 6.25 0.0615758184 4.5993138
## 535 0.214772809 6.75 0.2763486273 8.7628491
## 536 0.306921672 4.75 -0.0332908566 4.5117967
## 537 0.306921672 5.25 0.2736308152 4.5566192
## 538 0.306921672 5.75 0.5805524869 6.2544021
## 539 0.306921672 6.25 0.8874741587 6.0034901
## 540 0.306921672 6.75 1.1943958304 7.4461219
## 541 -0.077238288 4.00 -0.2023994705 3.8937881
## 542 -0.077238288 4.50 -0.2796377581 3.6523807
## 543 -0.077238288 5.00 -0.3568760457 5.4252434
## 544 -0.077238288 5.50 -0.4341143333 6.1866922
## 545 -0.077238288 6.00 -0.5113526209 7.7536208
## 546 -0.042625026 4.75 0.3991686915 4.5116564
## 547 -0.042625026 5.25 0.3565436658 6.2511125
## 548 -0.042625026 5.75 0.3139186400 4.3189038
## 549 -0.042625026 6.25 0.2712936143 8.1395614
## 550 -0.042625026 6.75 0.2286685886 7.1608371
## 551 -0.184341666 4.75 0.8768836919 4.1027702
## 552 -0.184341666 5.25 0.6925420261 5.7800164
## 553 -0.184341666 5.75 0.5082003603 6.1005236
## 554 -0.184341666 6.25 0.3238586945 7.3115972
## 555 -0.184341666 6.75 0.1395170287 7.9343422
## 556 0.106636350 4.00 0.4857230698 3.3975265
## 557 0.106636350 4.50 0.5923594202 5.2633550
## 558 0.106636350 5.00 0.6989957706 4.7811392
## 559 0.106636350 5.50 0.8056321210 5.6634600
## 560 0.106636350 6.00 0.9122684715 7.0752968
## 561 -0.027738075 4.00 -0.4024162810 3.4862866
## 562 -0.027738075 4.50 -0.4301543556 3.0333834
## 563 -0.027738075 5.00 -0.4578924301 4.6856548
## 564 -0.027738075 5.50 -0.4856305046 6.0882825
## 565 -0.027738075 6.00 -0.5133685792 6.1160156
## 566 0.094164328 4.00 -1.0138540887 2.4840257
## 567 0.094164328 4.50 -0.9196897609 3.2178357
## 568 0.094164328 5.00 -0.8255254331 4.9038627
## 569 0.094164328 5.50 -0.7313611053 4.5258975
## 570 0.094164328 6.00 -0.6371967775 5.6428773
## 571 0.011392344 4.75 -0.2403282449 5.3181198
## 572 0.011392344 5.25 -0.2289359007 3.8092732
## 573 0.011392344 5.75 -0.2175435566 6.3366076
## 574 0.011392344 6.25 -0.2061512124 5.1585698
## 575 0.011392344 6.75 -0.1947588683 5.9440678
## 576 0.659892987 4.75 0.0682876559 2.9838603
## 577 0.659892987 5.25 0.7281806430 5.7171204
## 578 0.659892987 5.75 1.3880736301 7.8243961
## 579 0.659892987 6.25 2.0479666172 8.1688964
## 580 0.659892987 6.75 2.7078596043 8.8592049
## 581 -0.841023607 4.75 -0.0343954947 2.7416699
## 582 -0.841023607 5.25 -0.8754191019 4.9560633
## 583 -0.841023607 5.75 -1.7164427091 3.1423727
## 584 -0.841023607 6.25 -2.5574663162 4.4022183
## 585 -0.841023607 6.75 -3.3984899234 3.0699886
## 586 0.482974457 4.00 0.1195491745 3.8633170
## 587 0.482974457 4.50 0.6025236316 4.9006679
## 588 0.482974457 5.00 1.0854980888 6.1325223
## 589 0.482974457 5.50 1.5684725460 8.6328358
## 590 0.482974457 6.00 2.0514470032 10.2062990
## 591 0.410388533 4.00 0.1128666646 4.9169464
## 592 0.410388533 4.50 0.5232551974 6.7774142
## 593 0.410388533 5.00 0.9336437302 7.8631054
## 594 0.410388533 5.50 1.3440322629 6.6365329
## 595 0.410388533 6.00 1.7544207957 9.0203903
## 596 0.056414063 4.00 -0.5603974486 3.6310390
## 597 0.056414063 4.50 -0.5039833856 2.0997101
## 598 0.056414063 5.00 -0.4475693226 3.8522814
## 599 0.056414063 5.50 -0.3911552595 3.0055355
## 600 0.056414063 6.00 -0.3347411965 5.1459897
## 601 -0.367914310 4.75 -0.6525352493 5.2599796
## 602 -0.367914310 5.25 -1.0204495597 4.4661776
## 603 -0.367914310 5.75 -1.3883638700 3.9218274
## 604 -0.367914310 6.25 -1.7562781803 4.6739022
## 605 -0.367914310 6.75 -2.1241924907 4.6865153
## 606 0.199448922 4.75 0.0482906482 3.5209671
## 607 0.199448922 5.25 0.2477395704 4.4160080
## 608 0.199448922 5.75 0.4471884926 6.4302378
## 609 0.199448922 6.25 0.6466374148 6.5554932
## 610 0.199448922 6.75 0.8460863370 5.9358530
## 611 -0.073913529 4.00 -0.0654885101 4.2255931
## 612 -0.073913529 4.50 -0.1394020393 5.1580830
## 613 -0.073913529 5.00 -0.2133155686 5.0644239
## 614 -0.073913529 5.50 -0.2872290978 3.8091883
## 615 -0.073913529 6.00 -0.3611426270 5.8745612
## 616 0.374012797 4.00 -0.2982347745 3.4636681
## 617 0.374012797 4.50 0.0757780226 4.2878212
## 618 0.374012797 5.00 0.4497908197 5.3085296
## 619 0.374012797 5.50 0.8238036168 6.0021009
## 620 0.374012797 6.00 1.1978164139 7.3940691
## 621 -0.197771059 4.75 0.0160241859 4.5117311
## 622 -0.197771059 5.25 -0.1817468736 5.5070541
## 623 -0.197771059 5.75 -0.3795179331 5.0108469
## 624 -0.197771059 6.25 -0.5772889926 4.2959460
## 625 -0.197771059 6.75 -0.7750600521 5.9647722
## 626 0.887146961 4.75 0.3618208905 5.4268334
## 627 0.887146961 5.25 1.2489678515 6.0805952
## 628 0.887146961 5.75 2.1361148126 7.3131016
## 629 0.887146961 6.25 3.0232617737 8.9294447
## 630 0.887146961 6.75 3.9104087347 11.5560234
## 631 -0.439544154 4.75 0.6635394825 2.6841332
## 632 -0.439544154 5.25 0.2239953284 5.4106606
## 633 -0.439544154 5.75 -0.2155488257 3.6598496
## 634 -0.439544154 6.25 -0.6550929799 6.5398072
## 635 -0.439544154 6.75 -1.0946371340 6.4894261
## 636 -0.153855753 4.00 0.3100331590 4.4614733
## 637 -0.153855753 4.50 0.1561774058 4.4932712
## 638 -0.153855753 5.00 0.0023216527 4.9084829
## 639 -0.153855753 5.50 -0.1515341005 5.1866906
## 640 -0.153855753 6.00 -0.3053898537 7.4641896
## 641 0.160356670 4.00 0.0144881975 4.0264902
## 642 0.160356670 4.50 0.1748448675 4.2736100
## 643 0.160356670 5.00 0.3352015375 5.9848930
## 644 0.160356670 5.50 0.4955582075 5.7143083
## 645 0.160356670 6.00 0.6559148775 9.8599652
## 646 -0.635398110 4.00 0.3251451943 5.4051323
## 647 -0.635398110 4.50 -0.3102529154 5.0528518
## 648 -0.635398110 5.00 -0.9456510250 4.6354186
## 649 -0.635398110 5.50 -1.5810491347 3.7776343
## 650 -0.635398110 6.00 -2.2164472444 2.6494724
## 651 -0.428055356 4.75 -0.4089918267 5.6798341
## 652 -0.428055356 5.25 -0.8370471823 2.7563483
## 653 -0.428055356 5.75 -1.2651025379 6.2233154
## 654 -0.428055356 6.25 -1.6931578935 4.3895321
## 655 -0.428055356 6.75 -2.1212132491 3.5296981
## 656 -0.712298672 4.00 -0.5229519934 4.4953544
## 657 -0.712298672 4.50 -1.2352506652 1.9565334
## 658 -0.712298672 5.00 -1.9475493371 4.9869961
## 659 -0.712298672 5.50 -2.6598480089 2.4787113
## 660 -0.712298672 6.00 -3.3721466807 2.1412633
## 661 -0.252489437 4.00 0.2519280851 2.8063343
## 662 -0.252489437 4.50 -0.0005613516 4.8630435
## 663 -0.252489437 5.00 -0.2530507883 4.0727740
## 664 -0.252489437 5.50 -0.5055402251 5.2560710
## 665 -0.252489437 6.00 -0.7580296618 4.1950002
## 666 -0.039901883 4.75 0.2856486032 6.9067450
## 667 -0.039901883 5.25 0.2457467199 5.7651336
## 668 -0.039901883 5.75 0.2058448365 6.0557918
## 669 -0.039901883 6.25 0.1659429531 8.9710285
## 670 -0.039901883 6.75 0.1260410697 6.7266435
## 671 -0.277410811 4.00 -0.1334762701 3.8803092
## 672 -0.277410811 4.50 -0.4108870809 4.7045548
## 673 -0.277410811 5.00 -0.6882978917 3.7313956
## 674 -0.277410811 5.50 -0.9657087025 5.3656260
## 675 -0.277410811 6.00 -1.2431195132 6.6684356
## 676 0.381352960 4.00 0.0823378089 3.3511282
## 677 0.381352960 4.50 0.4636907690 4.6360295
## 678 0.381352960 5.00 0.8450437291 5.2333707
## 679 0.381352960 5.50 1.2263966892 8.3236761
## 680 0.381352960 6.00 1.6077496493 7.3313524
## 681 -0.118661213 4.75 0.5718481123 4.8654510
## 682 -0.118661213 5.25 0.4531868990 8.0908352
## 683 -0.118661213 5.75 0.3345256856 6.2334391
## 684 -0.118661213 6.25 0.2158644722 7.2601419
## 685 -0.118661213 6.75 0.0972032588 7.4697487
## 686 -0.244401728 4.00 0.4793778393 5.5271047
## 687 -0.244401728 4.50 0.2349761112 5.9736296
## 688 -0.244401728 5.00 -0.0094256169 5.8636784
## 689 -0.244401728 5.50 -0.2538273449 5.9091268
## 690 -0.244401728 6.00 -0.4982290730 4.2825416
## 691 -0.208803166 4.00 0.1807979652 5.3847376
## 692 -0.208803166 4.50 -0.0280052012 2.6944055
## 693 -0.208803166 5.00 -0.2368083676 3.8146501
## 694 -0.208803166 5.50 -0.4456115340 5.0971472
## 695 -0.208803166 6.00 -0.6544147004 6.6019428
## 696 0.877051366 4.75 0.3298984399 4.5999135
## 697 0.877051366 5.25 1.2069498056 7.7211124
## 698 0.877051366 5.75 2.0840011713 7.0889506
## 699 0.877051366 6.25 2.9610525371 9.2246710
## 700 0.877051366 6.75 3.8381039028 10.2612632
## 701 -0.638961350 4.00 0.0357323387 2.2100323
## 702 -0.638961350 4.50 -0.6032290116 3.2776073
## 703 -0.638961350 5.00 -1.2421903618 3.9718166
## 704 -0.638961350 5.50 -1.8811517120 3.4628507
## 705 -0.638961350 6.00 -2.5201130623 4.9186606
## 706 -0.333812787 4.75 -0.3248291696 4.6546787
## 707 -0.333812787 5.25 -0.6586419561 4.4512825
## 708 -0.333812787 5.75 -0.9924547427 5.7918430
## 709 -0.333812787 6.25 -1.3262675292 5.2807275
## 710 -0.333812787 6.75 -1.6600803158 4.8606777
## 711 0.106921486 4.00 0.0369287116 4.4812764
## 712 0.106921486 4.50 0.1438501972 7.0083264
## 713 0.106921486 5.00 0.2507716829 4.6356006
## 714 0.106921486 5.50 0.3576931686 5.4513075
## 715 0.106921486 6.00 0.4646146542 6.7360335
## 716 0.152456114 4.75 0.6751159860 4.8633456
## 717 0.152456114 5.25 0.8275721001 5.5868627
## 718 0.152456114 5.75 0.9800282143 7.2413779
## 719 0.152456114 6.25 1.1324843285 6.3664049
## 720 0.152456114 6.75 1.2849404426 8.2395216
## 721 0.197232048 4.00 -0.0063697503 2.7481839
## 722 0.197232048 4.50 0.1908622973 3.1708114
## 723 0.197232048 5.00 0.3880943449 5.1420096
## 724 0.197232048 5.50 0.5853263925 6.2003135
## 725 0.197232048 6.00 0.7825584400 8.0760195
## 726 0.089270409 4.75 0.6147844532 5.2073959
## 727 0.089270409 5.25 0.7040548625 5.7684099
## 728 0.089270409 5.75 0.7933252718 6.1706200
## 729 0.089270409 6.25 0.8825956811 6.3513611
## 730 0.089270409 6.75 0.9718660904 7.2404970
## 731 0.525315432 4.75 -0.1935383482 4.9214057
## 732 0.525315432 5.25 0.3317770838 4.5804722
## 733 0.525315432 5.75 0.8570925159 6.6442268
## 734 0.525315432 6.25 1.3824079479 6.8947093
## 735 0.525315432 6.75 1.9077233800 7.4307274
## 736 0.255827698 4.75 0.7610560501 6.0528332
## 737 0.255827698 5.25 1.0168837480 6.5365348
## 738 0.255827698 5.75 1.2727114458 7.7561379
## 739 0.255827698 6.25 1.5285391437 8.4884237
## 740 0.255827698 6.75 1.7843668416 8.1887718
## 741 -0.390560358 4.75 -0.0943714417 3.4316877
## 742 -0.390560358 5.25 -0.4849318001 3.8765015
## 743 -0.390560358 5.75 -0.8754921585 6.6171698
## 744 -0.390560358 6.25 -1.2660525169 5.1083876
## 745 -0.390560358 6.75 -1.6566128754 5.9923729
## 746 0.489239100 4.00 0.2575463473 5.1340260
## 747 0.489239100 4.50 0.7467854470 5.0553475
## 748 0.489239100 5.00 1.2360245467 5.9429261
## 749 0.489239100 5.50 1.7252636464 6.9870937
## 750 0.489239100 6.00 2.2145027461 7.3765170
##
## [[2]]
## sample_size replication X.Intercept. time trt_1
## 1 list(level1 = 5, level2 = 60) 1 1 0 1
## 2 list(level1 = 5, level2 = 60) 1 1 1 1
## 3 list(level1 = 5, level2 = 60) 1 1 2 1
## 4 list(level1 = 5, level2 = 60) 1 1 3 1
## 5 list(level1 = 5, level2 = 60) 1 1 4 1
## 6 list(level1 = 5, level2 = 60) 1 1 0 0
## 7 list(level1 = 5, level2 = 60) 1 1 1 0
## 8 list(level1 = 5, level2 = 60) 1 1 2 0
## 9 list(level1 = 5, level2 = 60) 1 1 3 0
## 10 list(level1 = 5, level2 = 60) 1 1 4 0
## 11 list(level1 = 5, level2 = 60) 1 1 0 1
## 12 list(level1 = 5, level2 = 60) 1 1 1 1
## 13 list(level1 = 5, level2 = 60) 1 1 2 1
## 14 list(level1 = 5, level2 = 60) 1 1 3 1
## 15 list(level1 = 5, level2 = 60) 1 1 4 1
## 16 list(level1 = 5, level2 = 60) 1 1 0 0
## 17 list(level1 = 5, level2 = 60) 1 1 1 0
## 18 list(level1 = 5, level2 = 60) 1 1 2 0
## 19 list(level1 = 5, level2 = 60) 1 1 3 0
## 20 list(level1 = 5, level2 = 60) 1 1 4 0
## 21 list(level1 = 5, level2 = 60) 1 1 0 0
## 22 list(level1 = 5, level2 = 60) 1 1 1 0
## 23 list(level1 = 5, level2 = 60) 1 1 2 0
## 24 list(level1 = 5, level2 = 60) 1 1 3 0
## 25 list(level1 = 5, level2 = 60) 1 1 4 0
## 26 list(level1 = 5, level2 = 60) 1 1 0 1
## 27 list(level1 = 5, level2 = 60) 1 1 1 1
## 28 list(level1 = 5, level2 = 60) 1 1 2 1
## 29 list(level1 = 5, level2 = 60) 1 1 3 1
## 30 list(level1 = 5, level2 = 60) 1 1 4 1
## 31 list(level1 = 5, level2 = 60) 1 1 0 1
## 32 list(level1 = 5, level2 = 60) 1 1 1 1
## 33 list(level1 = 5, level2 = 60) 1 1 2 1
## 34 list(level1 = 5, level2 = 60) 1 1 3 1
## 35 list(level1 = 5, level2 = 60) 1 1 4 1
## 36 list(level1 = 5, level2 = 60) 1 1 0 0
## 37 list(level1 = 5, level2 = 60) 1 1 1 0
## 38 list(level1 = 5, level2 = 60) 1 1 2 0
## 39 list(level1 = 5, level2 = 60) 1 1 3 0
## 40 list(level1 = 5, level2 = 60) 1 1 4 0
## 41 list(level1 = 5, level2 = 60) 1 1 0 1
## 42 list(level1 = 5, level2 = 60) 1 1 1 1
## 43 list(level1 = 5, level2 = 60) 1 1 2 1
## 44 list(level1 = 5, level2 = 60) 1 1 3 1
## 45 list(level1 = 5, level2 = 60) 1 1 4 1
## 46 list(level1 = 5, level2 = 60) 1 1 0 0
## 47 list(level1 = 5, level2 = 60) 1 1 1 0
## 48 list(level1 = 5, level2 = 60) 1 1 2 0
## 49 list(level1 = 5, level2 = 60) 1 1 3 0
## 50 list(level1 = 5, level2 = 60) 1 1 4 0
## 51 list(level1 = 5, level2 = 60) 1 1 0 1
## 52 list(level1 = 5, level2 = 60) 1 1 1 1
## 53 list(level1 = 5, level2 = 60) 1 1 2 1
## 54 list(level1 = 5, level2 = 60) 1 1 3 1
## 55 list(level1 = 5, level2 = 60) 1 1 4 1
## 56 list(level1 = 5, level2 = 60) 1 1 0 1
## 57 list(level1 = 5, level2 = 60) 1 1 1 1
## 58 list(level1 = 5, level2 = 60) 1 1 2 1
## 59 list(level1 = 5, level2 = 60) 1 1 3 1
## 60 list(level1 = 5, level2 = 60) 1 1 4 1
## 61 list(level1 = 5, level2 = 60) 1 1 0 1
## 62 list(level1 = 5, level2 = 60) 1 1 1 1
## 63 list(level1 = 5, level2 = 60) 1 1 2 1
## 64 list(level1 = 5, level2 = 60) 1 1 3 1
## 65 list(level1 = 5, level2 = 60) 1 1 4 1
## 66 list(level1 = 5, level2 = 60) 1 1 0 0
## 67 list(level1 = 5, level2 = 60) 1 1 1 0
## 68 list(level1 = 5, level2 = 60) 1 1 2 0
## 69 list(level1 = 5, level2 = 60) 1 1 3 0
## 70 list(level1 = 5, level2 = 60) 1 1 4 0
## 71 list(level1 = 5, level2 = 60) 1 1 0 0
## 72 list(level1 = 5, level2 = 60) 1 1 1 0
## 73 list(level1 = 5, level2 = 60) 1 1 2 0
## 74 list(level1 = 5, level2 = 60) 1 1 3 0
## 75 list(level1 = 5, level2 = 60) 1 1 4 0
## 76 list(level1 = 5, level2 = 60) 1 1 0 0
## 77 list(level1 = 5, level2 = 60) 1 1 1 0
## 78 list(level1 = 5, level2 = 60) 1 1 2 0
## 79 list(level1 = 5, level2 = 60) 1 1 3 0
## 80 list(level1 = 5, level2 = 60) 1 1 4 0
## 81 list(level1 = 5, level2 = 60) 1 1 0 1
## 82 list(level1 = 5, level2 = 60) 1 1 1 1
## 83 list(level1 = 5, level2 = 60) 1 1 2 1
## 84 list(level1 = 5, level2 = 60) 1 1 3 1
## 85 list(level1 = 5, level2 = 60) 1 1 4 1
## 86 list(level1 = 5, level2 = 60) 1 1 0 1
## 87 list(level1 = 5, level2 = 60) 1 1 1 1
## 88 list(level1 = 5, level2 = 60) 1 1 2 1
## 89 list(level1 = 5, level2 = 60) 1 1 3 1
## 90 list(level1 = 5, level2 = 60) 1 1 4 1
## 91 list(level1 = 5, level2 = 60) 1 1 0 0
## 92 list(level1 = 5, level2 = 60) 1 1 1 0
## 93 list(level1 = 5, level2 = 60) 1 1 2 0
## 94 list(level1 = 5, level2 = 60) 1 1 3 0
## 95 list(level1 = 5, level2 = 60) 1 1 4 0
## 96 list(level1 = 5, level2 = 60) 1 1 0 1
## 97 list(level1 = 5, level2 = 60) 1 1 1 1
## 98 list(level1 = 5, level2 = 60) 1 1 2 1
## 99 list(level1 = 5, level2 = 60) 1 1 3 1
## 100 list(level1 = 5, level2 = 60) 1 1 4 1
## 101 list(level1 = 5, level2 = 60) 1 1 0 0
## 102 list(level1 = 5, level2 = 60) 1 1 1 0
## 103 list(level1 = 5, level2 = 60) 1 1 2 0
## 104 list(level1 = 5, level2 = 60) 1 1 3 0
## 105 list(level1 = 5, level2 = 60) 1 1 4 0
## 106 list(level1 = 5, level2 = 60) 1 1 0 0
## 107 list(level1 = 5, level2 = 60) 1 1 1 0
## 108 list(level1 = 5, level2 = 60) 1 1 2 0
## 109 list(level1 = 5, level2 = 60) 1 1 3 0
## 110 list(level1 = 5, level2 = 60) 1 1 4 0
## 111 list(level1 = 5, level2 = 60) 1 1 0 1
## 112 list(level1 = 5, level2 = 60) 1 1 1 1
## 113 list(level1 = 5, level2 = 60) 1 1 2 1
## 114 list(level1 = 5, level2 = 60) 1 1 3 1
## 115 list(level1 = 5, level2 = 60) 1 1 4 1
## 116 list(level1 = 5, level2 = 60) 1 1 0 0
## 117 list(level1 = 5, level2 = 60) 1 1 1 0
## 118 list(level1 = 5, level2 = 60) 1 1 2 0
## 119 list(level1 = 5, level2 = 60) 1 1 3 0
## 120 list(level1 = 5, level2 = 60) 1 1 4 0
## 121 list(level1 = 5, level2 = 60) 1 1 0 1
## 122 list(level1 = 5, level2 = 60) 1 1 1 1
## 123 list(level1 = 5, level2 = 60) 1 1 2 1
## 124 list(level1 = 5, level2 = 60) 1 1 3 1
## 125 list(level1 = 5, level2 = 60) 1 1 4 1
## 126 list(level1 = 5, level2 = 60) 1 1 0 0
## 127 list(level1 = 5, level2 = 60) 1 1 1 0
## 128 list(level1 = 5, level2 = 60) 1 1 2 0
## 129 list(level1 = 5, level2 = 60) 1 1 3 0
## 130 list(level1 = 5, level2 = 60) 1 1 4 0
## 131 list(level1 = 5, level2 = 60) 1 1 0 1
## 132 list(level1 = 5, level2 = 60) 1 1 1 1
## 133 list(level1 = 5, level2 = 60) 1 1 2 1
## 134 list(level1 = 5, level2 = 60) 1 1 3 1
## 135 list(level1 = 5, level2 = 60) 1 1 4 1
## 136 list(level1 = 5, level2 = 60) 1 1 0 0
## 137 list(level1 = 5, level2 = 60) 1 1 1 0
## 138 list(level1 = 5, level2 = 60) 1 1 2 0
## 139 list(level1 = 5, level2 = 60) 1 1 3 0
## 140 list(level1 = 5, level2 = 60) 1 1 4 0
## 141 list(level1 = 5, level2 = 60) 1 1 0 0
## 142 list(level1 = 5, level2 = 60) 1 1 1 0
## 143 list(level1 = 5, level2 = 60) 1 1 2 0
## 144 list(level1 = 5, level2 = 60) 1 1 3 0
## 145 list(level1 = 5, level2 = 60) 1 1 4 0
## 146 list(level1 = 5, level2 = 60) 1 1 0 1
## 147 list(level1 = 5, level2 = 60) 1 1 1 1
## 148 list(level1 = 5, level2 = 60) 1 1 2 1
## 149 list(level1 = 5, level2 = 60) 1 1 3 1
## 150 list(level1 = 5, level2 = 60) 1 1 4 1
## 151 list(level1 = 5, level2 = 60) 1 1 0 1
## 152 list(level1 = 5, level2 = 60) 1 1 1 1
## 153 list(level1 = 5, level2 = 60) 1 1 2 1
## 154 list(level1 = 5, level2 = 60) 1 1 3 1
## 155 list(level1 = 5, level2 = 60) 1 1 4 1
## 156 list(level1 = 5, level2 = 60) 1 1 0 0
## 157 list(level1 = 5, level2 = 60) 1 1 1 0
## 158 list(level1 = 5, level2 = 60) 1 1 2 0
## 159 list(level1 = 5, level2 = 60) 1 1 3 0
## 160 list(level1 = 5, level2 = 60) 1 1 4 0
## 161 list(level1 = 5, level2 = 60) 1 1 0 0
## 162 list(level1 = 5, level2 = 60) 1 1 1 0
## 163 list(level1 = 5, level2 = 60) 1 1 2 0
## 164 list(level1 = 5, level2 = 60) 1 1 3 0
## 165 list(level1 = 5, level2 = 60) 1 1 4 0
## 166 list(level1 = 5, level2 = 60) 1 1 0 0
## 167 list(level1 = 5, level2 = 60) 1 1 1 0
## 168 list(level1 = 5, level2 = 60) 1 1 2 0
## 169 list(level1 = 5, level2 = 60) 1 1 3 0
## 170 list(level1 = 5, level2 = 60) 1 1 4 0
## 171 list(level1 = 5, level2 = 60) 1 1 0 1
## 172 list(level1 = 5, level2 = 60) 1 1 1 1
## 173 list(level1 = 5, level2 = 60) 1 1 2 1
## 174 list(level1 = 5, level2 = 60) 1 1 3 1
## 175 list(level1 = 5, level2 = 60) 1 1 4 1
## 176 list(level1 = 5, level2 = 60) 1 1 0 1
## 177 list(level1 = 5, level2 = 60) 1 1 1 1
## 178 list(level1 = 5, level2 = 60) 1 1 2 1
## 179 list(level1 = 5, level2 = 60) 1 1 3 1
## 180 list(level1 = 5, level2 = 60) 1 1 4 1
## 181 list(level1 = 5, level2 = 60) 1 1 0 1
## 182 list(level1 = 5, level2 = 60) 1 1 1 1
## 183 list(level1 = 5, level2 = 60) 1 1 2 1
## 184 list(level1 = 5, level2 = 60) 1 1 3 1
## 185 list(level1 = 5, level2 = 60) 1 1 4 1
## 186 list(level1 = 5, level2 = 60) 1 1 0 0
## 187 list(level1 = 5, level2 = 60) 1 1 1 0
## 188 list(level1 = 5, level2 = 60) 1 1 2 0
## 189 list(level1 = 5, level2 = 60) 1 1 3 0
## 190 list(level1 = 5, level2 = 60) 1 1 4 0
## 191 list(level1 = 5, level2 = 60) 1 1 0 1
## 192 list(level1 = 5, level2 = 60) 1 1 1 1
## 193 list(level1 = 5, level2 = 60) 1 1 2 1
## 194 list(level1 = 5, level2 = 60) 1 1 3 1
## 195 list(level1 = 5, level2 = 60) 1 1 4 1
## 196 list(level1 = 5, level2 = 60) 1 1 0 1
## 197 list(level1 = 5, level2 = 60) 1 1 1 1
## 198 list(level1 = 5, level2 = 60) 1 1 2 1
## 199 list(level1 = 5, level2 = 60) 1 1 3 1
## 200 list(level1 = 5, level2 = 60) 1 1 4 1
## 201 list(level1 = 5, level2 = 60) 1 1 0 1
## 202 list(level1 = 5, level2 = 60) 1 1 1 1
## 203 list(level1 = 5, level2 = 60) 1 1 2 1
## 204 list(level1 = 5, level2 = 60) 1 1 3 1
## 205 list(level1 = 5, level2 = 60) 1 1 4 1
## 206 list(level1 = 5, level2 = 60) 1 1 0 0
## 207 list(level1 = 5, level2 = 60) 1 1 1 0
## 208 list(level1 = 5, level2 = 60) 1 1 2 0
## 209 list(level1 = 5, level2 = 60) 1 1 3 0
## 210 list(level1 = 5, level2 = 60) 1 1 4 0
## 211 list(level1 = 5, level2 = 60) 1 1 0 0
## 212 list(level1 = 5, level2 = 60) 1 1 1 0
## 213 list(level1 = 5, level2 = 60) 1 1 2 0
## 214 list(level1 = 5, level2 = 60) 1 1 3 0
## 215 list(level1 = 5, level2 = 60) 1 1 4 0
## 216 list(level1 = 5, level2 = 60) 1 1 0 0
## 217 list(level1 = 5, level2 = 60) 1 1 1 0
## 218 list(level1 = 5, level2 = 60) 1 1 2 0
## 219 list(level1 = 5, level2 = 60) 1 1 3 0
## 220 list(level1 = 5, level2 = 60) 1 1 4 0
## 221 list(level1 = 5, level2 = 60) 1 1 0 1
## 222 list(level1 = 5, level2 = 60) 1 1 1 1
## 223 list(level1 = 5, level2 = 60) 1 1 2 1
## 224 list(level1 = 5, level2 = 60) 1 1 3 1
## 225 list(level1 = 5, level2 = 60) 1 1 4 1
## 226 list(level1 = 5, level2 = 60) 1 1 0 0
## 227 list(level1 = 5, level2 = 60) 1 1 1 0
## 228 list(level1 = 5, level2 = 60) 1 1 2 0
## 229 list(level1 = 5, level2 = 60) 1 1 3 0
## 230 list(level1 = 5, level2 = 60) 1 1 4 0
## 231 list(level1 = 5, level2 = 60) 1 1 0 1
## 232 list(level1 = 5, level2 = 60) 1 1 1 1
## 233 list(level1 = 5, level2 = 60) 1 1 2 1
## 234 list(level1 = 5, level2 = 60) 1 1 3 1
## 235 list(level1 = 5, level2 = 60) 1 1 4 1
## 236 list(level1 = 5, level2 = 60) 1 1 0 0
## 237 list(level1 = 5, level2 = 60) 1 1 1 0
## 238 list(level1 = 5, level2 = 60) 1 1 2 0
## 239 list(level1 = 5, level2 = 60) 1 1 3 0
## 240 list(level1 = 5, level2 = 60) 1 1 4 0
## 241 list(level1 = 5, level2 = 60) 1 1 0 1
## 242 list(level1 = 5, level2 = 60) 1 1 1 1
## 243 list(level1 = 5, level2 = 60) 1 1 2 1
## 244 list(level1 = 5, level2 = 60) 1 1 3 1
## 245 list(level1 = 5, level2 = 60) 1 1 4 1
## 246 list(level1 = 5, level2 = 60) 1 1 0 1
## 247 list(level1 = 5, level2 = 60) 1 1 1 1
## 248 list(level1 = 5, level2 = 60) 1 1 2 1
## 249 list(level1 = 5, level2 = 60) 1 1 3 1
## 250 list(level1 = 5, level2 = 60) 1 1 4 1
## 251 list(level1 = 5, level2 = 60) 1 1 0 0
## 252 list(level1 = 5, level2 = 60) 1 1 1 0
## 253 list(level1 = 5, level2 = 60) 1 1 2 0
## 254 list(level1 = 5, level2 = 60) 1 1 3 0
## 255 list(level1 = 5, level2 = 60) 1 1 4 0
## 256 list(level1 = 5, level2 = 60) 1 1 0 0
## 257 list(level1 = 5, level2 = 60) 1 1 1 0
## 258 list(level1 = 5, level2 = 60) 1 1 2 0
## 259 list(level1 = 5, level2 = 60) 1 1 3 0
## 260 list(level1 = 5, level2 = 60) 1 1 4 0
## 261 list(level1 = 5, level2 = 60) 1 1 0 0
## 262 list(level1 = 5, level2 = 60) 1 1 1 0
## 263 list(level1 = 5, level2 = 60) 1 1 2 0
## 264 list(level1 = 5, level2 = 60) 1 1 3 0
## 265 list(level1 = 5, level2 = 60) 1 1 4 0
## 266 list(level1 = 5, level2 = 60) 1 1 0 0
## 267 list(level1 = 5, level2 = 60) 1 1 1 0
## 268 list(level1 = 5, level2 = 60) 1 1 2 0
## 269 list(level1 = 5, level2 = 60) 1 1 3 0
## 270 list(level1 = 5, level2 = 60) 1 1 4 0
## 271 list(level1 = 5, level2 = 60) 1 1 0 1
## 272 list(level1 = 5, level2 = 60) 1 1 1 1
## 273 list(level1 = 5, level2 = 60) 1 1 2 1
## 274 list(level1 = 5, level2 = 60) 1 1 3 1
## 275 list(level1 = 5, level2 = 60) 1 1 4 1
## 276 list(level1 = 5, level2 = 60) 1 1 0 1
## 277 list(level1 = 5, level2 = 60) 1 1 1 1
## 278 list(level1 = 5, level2 = 60) 1 1 2 1
## 279 list(level1 = 5, level2 = 60) 1 1 3 1
## 280 list(level1 = 5, level2 = 60) 1 1 4 1
## 281 list(level1 = 5, level2 = 60) 1 1 0 1
## 282 list(level1 = 5, level2 = 60) 1 1 1 1
## 283 list(level1 = 5, level2 = 60) 1 1 2 1
## 284 list(level1 = 5, level2 = 60) 1 1 3 1
## 285 list(level1 = 5, level2 = 60) 1 1 4 1
## 286 list(level1 = 5, level2 = 60) 1 1 0 0
## 287 list(level1 = 5, level2 = 60) 1 1 1 0
## 288 list(level1 = 5, level2 = 60) 1 1 2 0
## 289 list(level1 = 5, level2 = 60) 1 1 3 0
## 290 list(level1 = 5, level2 = 60) 1 1 4 0
## 291 list(level1 = 5, level2 = 60) 1 1 0 0
## 292 list(level1 = 5, level2 = 60) 1 1 1 0
## 293 list(level1 = 5, level2 = 60) 1 1 2 0
## 294 list(level1 = 5, level2 = 60) 1 1 3 0
## 295 list(level1 = 5, level2 = 60) 1 1 4 0
## 296 list(level1 = 5, level2 = 60) 1 1 0 0
## 297 list(level1 = 5, level2 = 60) 1 1 1 0
## 298 list(level1 = 5, level2 = 60) 1 1 2 0
## 299 list(level1 = 5, level2 = 60) 1 1 3 0
## 300 list(level1 = 5, level2 = 60) 1 1 4 0
## 301 list(level1 = 5, level2 = 60) 2 1 0 0
## 302 list(level1 = 5, level2 = 60) 2 1 1 0
## 303 list(level1 = 5, level2 = 60) 2 1 2 0
## 304 list(level1 = 5, level2 = 60) 2 1 3 0
## 305 list(level1 = 5, level2 = 60) 2 1 4 0
## 306 list(level1 = 5, level2 = 60) 2 1 0 0
## 307 list(level1 = 5, level2 = 60) 2 1 1 0
## 308 list(level1 = 5, level2 = 60) 2 1 2 0
## 309 list(level1 = 5, level2 = 60) 2 1 3 0
## 310 list(level1 = 5, level2 = 60) 2 1 4 0
## 311 list(level1 = 5, level2 = 60) 2 1 0 1
## 312 list(level1 = 5, level2 = 60) 2 1 1 1
## 313 list(level1 = 5, level2 = 60) 2 1 2 1
## 314 list(level1 = 5, level2 = 60) 2 1 3 1
## 315 list(level1 = 5, level2 = 60) 2 1 4 1
## 316 list(level1 = 5, level2 = 60) 2 1 0 0
## 317 list(level1 = 5, level2 = 60) 2 1 1 0
## 318 list(level1 = 5, level2 = 60) 2 1 2 0
## 319 list(level1 = 5, level2 = 60) 2 1 3 0
## 320 list(level1 = 5, level2 = 60) 2 1 4 0
## 321 list(level1 = 5, level2 = 60) 2 1 0 0
## 322 list(level1 = 5, level2 = 60) 2 1 1 0
## 323 list(level1 = 5, level2 = 60) 2 1 2 0
## 324 list(level1 = 5, level2 = 60) 2 1 3 0
## 325 list(level1 = 5, level2 = 60) 2 1 4 0
## 326 list(level1 = 5, level2 = 60) 2 1 0 1
## 327 list(level1 = 5, level2 = 60) 2 1 1 1
## 328 list(level1 = 5, level2 = 60) 2 1 2 1
## 329 list(level1 = 5, level2 = 60) 2 1 3 1
## 330 list(level1 = 5, level2 = 60) 2 1 4 1
## 331 list(level1 = 5, level2 = 60) 2 1 0 1
## 332 list(level1 = 5, level2 = 60) 2 1 1 1
## 333 list(level1 = 5, level2 = 60) 2 1 2 1
## 334 list(level1 = 5, level2 = 60) 2 1 3 1
## 335 list(level1 = 5, level2 = 60) 2 1 4 1
## 336 list(level1 = 5, level2 = 60) 2 1 0 1
## 337 list(level1 = 5, level2 = 60) 2 1 1 1
## 338 list(level1 = 5, level2 = 60) 2 1 2 1
## 339 list(level1 = 5, level2 = 60) 2 1 3 1
## 340 list(level1 = 5, level2 = 60) 2 1 4 1
## 341 list(level1 = 5, level2 = 60) 2 1 0 0
## 342 list(level1 = 5, level2 = 60) 2 1 1 0
## 343 list(level1 = 5, level2 = 60) 2 1 2 0
## 344 list(level1 = 5, level2 = 60) 2 1 3 0
## 345 list(level1 = 5, level2 = 60) 2 1 4 0
## 346 list(level1 = 5, level2 = 60) 2 1 0 1
## 347 list(level1 = 5, level2 = 60) 2 1 1 1
## 348 list(level1 = 5, level2 = 60) 2 1 2 1
## 349 list(level1 = 5, level2 = 60) 2 1 3 1
## 350 list(level1 = 5, level2 = 60) 2 1 4 1
## 351 list(level1 = 5, level2 = 60) 2 1 0 1
## 352 list(level1 = 5, level2 = 60) 2 1 1 1
## 353 list(level1 = 5, level2 = 60) 2 1 2 1
## 354 list(level1 = 5, level2 = 60) 2 1 3 1
## 355 list(level1 = 5, level2 = 60) 2 1 4 1
## 356 list(level1 = 5, level2 = 60) 2 1 0 0
## 357 list(level1 = 5, level2 = 60) 2 1 1 0
## 358 list(level1 = 5, level2 = 60) 2 1 2 0
## 359 list(level1 = 5, level2 = 60) 2 1 3 0
## 360 list(level1 = 5, level2 = 60) 2 1 4 0
## 361 list(level1 = 5, level2 = 60) 2 1 0 0
## 362 list(level1 = 5, level2 = 60) 2 1 1 0
## 363 list(level1 = 5, level2 = 60) 2 1 2 0
## 364 list(level1 = 5, level2 = 60) 2 1 3 0
## 365 list(level1 = 5, level2 = 60) 2 1 4 0
## 366 list(level1 = 5, level2 = 60) 2 1 0 0
## 367 list(level1 = 5, level2 = 60) 2 1 1 0
## 368 list(level1 = 5, level2 = 60) 2 1 2 0
## 369 list(level1 = 5, level2 = 60) 2 1 3 0
## 370 list(level1 = 5, level2 = 60) 2 1 4 0
## 371 list(level1 = 5, level2 = 60) 2 1 0 1
## 372 list(level1 = 5, level2 = 60) 2 1 1 1
## 373 list(level1 = 5, level2 = 60) 2 1 2 1
## 374 list(level1 = 5, level2 = 60) 2 1 3 1
## 375 list(level1 = 5, level2 = 60) 2 1 4 1
## 376 list(level1 = 5, level2 = 60) 2 1 0 1
## 377 list(level1 = 5, level2 = 60) 2 1 1 1
## 378 list(level1 = 5, level2 = 60) 2 1 2 1
## 379 list(level1 = 5, level2 = 60) 2 1 3 1
## 380 list(level1 = 5, level2 = 60) 2 1 4 1
## 381 list(level1 = 5, level2 = 60) 2 1 0 1
## 382 list(level1 = 5, level2 = 60) 2 1 1 1
## 383 list(level1 = 5, level2 = 60) 2 1 2 1
## 384 list(level1 = 5, level2 = 60) 2 1 3 1
## 385 list(level1 = 5, level2 = 60) 2 1 4 1
## 386 list(level1 = 5, level2 = 60) 2 1 0 0
## 387 list(level1 = 5, level2 = 60) 2 1 1 0
## 388 list(level1 = 5, level2 = 60) 2 1 2 0
## 389 list(level1 = 5, level2 = 60) 2 1 3 0
## 390 list(level1 = 5, level2 = 60) 2 1 4 0
## 391 list(level1 = 5, level2 = 60) 2 1 0 0
## 392 list(level1 = 5, level2 = 60) 2 1 1 0
## 393 list(level1 = 5, level2 = 60) 2 1 2 0
## 394 list(level1 = 5, level2 = 60) 2 1 3 0
## 395 list(level1 = 5, level2 = 60) 2 1 4 0
## 396 list(level1 = 5, level2 = 60) 2 1 0 0
## 397 list(level1 = 5, level2 = 60) 2 1 1 0
## 398 list(level1 = 5, level2 = 60) 2 1 2 0
## 399 list(level1 = 5, level2 = 60) 2 1 3 0
## 400 list(level1 = 5, level2 = 60) 2 1 4 0
## 401 list(level1 = 5, level2 = 60) 2 1 0 1
## 402 list(level1 = 5, level2 = 60) 2 1 1 1
## 403 list(level1 = 5, level2 = 60) 2 1 2 1
## 404 list(level1 = 5, level2 = 60) 2 1 3 1
## 405 list(level1 = 5, level2 = 60) 2 1 4 1
## 406 list(level1 = 5, level2 = 60) 2 1 0 1
## 407 list(level1 = 5, level2 = 60) 2 1 1 1
## 408 list(level1 = 5, level2 = 60) 2 1 2 1
## 409 list(level1 = 5, level2 = 60) 2 1 3 1
## 410 list(level1 = 5, level2 = 60) 2 1 4 1
## 411 list(level1 = 5, level2 = 60) 2 1 0 0
## 412 list(level1 = 5, level2 = 60) 2 1 1 0
## 413 list(level1 = 5, level2 = 60) 2 1 2 0
## 414 list(level1 = 5, level2 = 60) 2 1 3 0
## 415 list(level1 = 5, level2 = 60) 2 1 4 0
## 416 list(level1 = 5, level2 = 60) 2 1 0 0
## 417 list(level1 = 5, level2 = 60) 2 1 1 0
## 418 list(level1 = 5, level2 = 60) 2 1 2 0
## 419 list(level1 = 5, level2 = 60) 2 1 3 0
## 420 list(level1 = 5, level2 = 60) 2 1 4 0
## 421 list(level1 = 5, level2 = 60) 2 1 0 1
## 422 list(level1 = 5, level2 = 60) 2 1 1 1
## 423 list(level1 = 5, level2 = 60) 2 1 2 1
## 424 list(level1 = 5, level2 = 60) 2 1 3 1
## 425 list(level1 = 5, level2 = 60) 2 1 4 1
## 426 list(level1 = 5, level2 = 60) 2 1 0 1
## 427 list(level1 = 5, level2 = 60) 2 1 1 1
## 428 list(level1 = 5, level2 = 60) 2 1 2 1
## 429 list(level1 = 5, level2 = 60) 2 1 3 1
## 430 list(level1 = 5, level2 = 60) 2 1 4 1
## 431 list(level1 = 5, level2 = 60) 2 1 0 1
## 432 list(level1 = 5, level2 = 60) 2 1 1 1
## 433 list(level1 = 5, level2 = 60) 2 1 2 1
## 434 list(level1 = 5, level2 = 60) 2 1 3 1
## 435 list(level1 = 5, level2 = 60) 2 1 4 1
## 436 list(level1 = 5, level2 = 60) 2 1 0 0
## 437 list(level1 = 5, level2 = 60) 2 1 1 0
## 438 list(level1 = 5, level2 = 60) 2 1 2 0
## 439 list(level1 = 5, level2 = 60) 2 1 3 0
## 440 list(level1 = 5, level2 = 60) 2 1 4 0
## 441 list(level1 = 5, level2 = 60) 2 1 0 0
## 442 list(level1 = 5, level2 = 60) 2 1 1 0
## 443 list(level1 = 5, level2 = 60) 2 1 2 0
## 444 list(level1 = 5, level2 = 60) 2 1 3 0
## 445 list(level1 = 5, level2 = 60) 2 1 4 0
## 446 list(level1 = 5, level2 = 60) 2 1 0 0
## 447 list(level1 = 5, level2 = 60) 2 1 1 0
## 448 list(level1 = 5, level2 = 60) 2 1 2 0
## 449 list(level1 = 5, level2 = 60) 2 1 3 0
## 450 list(level1 = 5, level2 = 60) 2 1 4 0
## 451 list(level1 = 5, level2 = 60) 2 1 0 1
## 452 list(level1 = 5, level2 = 60) 2 1 1 1
## 453 list(level1 = 5, level2 = 60) 2 1 2 1
## 454 list(level1 = 5, level2 = 60) 2 1 3 1
## 455 list(level1 = 5, level2 = 60) 2 1 4 1
## 456 list(level1 = 5, level2 = 60) 2 1 0 0
## 457 list(level1 = 5, level2 = 60) 2 1 1 0
## 458 list(level1 = 5, level2 = 60) 2 1 2 0
## 459 list(level1 = 5, level2 = 60) 2 1 3 0
## 460 list(level1 = 5, level2 = 60) 2 1 4 0
## 461 list(level1 = 5, level2 = 60) 2 1 0 0
## 462 list(level1 = 5, level2 = 60) 2 1 1 0
## 463 list(level1 = 5, level2 = 60) 2 1 2 0
## 464 list(level1 = 5, level2 = 60) 2 1 3 0
## 465 list(level1 = 5, level2 = 60) 2 1 4 0
## 466 list(level1 = 5, level2 = 60) 2 1 0 1
## 467 list(level1 = 5, level2 = 60) 2 1 1 1
## 468 list(level1 = 5, level2 = 60) 2 1 2 1
## 469 list(level1 = 5, level2 = 60) 2 1 3 1
## 470 list(level1 = 5, level2 = 60) 2 1 4 1
## 471 list(level1 = 5, level2 = 60) 2 1 0 0
## 472 list(level1 = 5, level2 = 60) 2 1 1 0
## 473 list(level1 = 5, level2 = 60) 2 1 2 0
## 474 list(level1 = 5, level2 = 60) 2 1 3 0
## 475 list(level1 = 5, level2 = 60) 2 1 4 0
## 476 list(level1 = 5, level2 = 60) 2 1 0 0
## 477 list(level1 = 5, level2 = 60) 2 1 1 0
## 478 list(level1 = 5, level2 = 60) 2 1 2 0
## 479 list(level1 = 5, level2 = 60) 2 1 3 0
## 480 list(level1 = 5, level2 = 60) 2 1 4 0
## 481 list(level1 = 5, level2 = 60) 2 1 0 1
## 482 list(level1 = 5, level2 = 60) 2 1 1 1
## 483 list(level1 = 5, level2 = 60) 2 1 2 1
## 484 list(level1 = 5, level2 = 60) 2 1 3 1
## 485 list(level1 = 5, level2 = 60) 2 1 4 1
## 486 list(level1 = 5, level2 = 60) 2 1 0 0
## 487 list(level1 = 5, level2 = 60) 2 1 1 0
## 488 list(level1 = 5, level2 = 60) 2 1 2 0
## 489 list(level1 = 5, level2 = 60) 2 1 3 0
## 490 list(level1 = 5, level2 = 60) 2 1 4 0
## 491 list(level1 = 5, level2 = 60) 2 1 0 0
## 492 list(level1 = 5, level2 = 60) 2 1 1 0
## 493 list(level1 = 5, level2 = 60) 2 1 2 0
## 494 list(level1 = 5, level2 = 60) 2 1 3 0
## 495 list(level1 = 5, level2 = 60) 2 1 4 0
## 496 list(level1 = 5, level2 = 60) 2 1 0 1
## 497 list(level1 = 5, level2 = 60) 2 1 1 1
## 498 list(level1 = 5, level2 = 60) 2 1 2 1
## 499 list(level1 = 5, level2 = 60) 2 1 3 1
## 500 list(level1 = 5, level2 = 60) 2 1 4 1
## 501 list(level1 = 5, level2 = 60) 2 1 0 0
## 502 list(level1 = 5, level2 = 60) 2 1 1 0
## 503 list(level1 = 5, level2 = 60) 2 1 2 0
## 504 list(level1 = 5, level2 = 60) 2 1 3 0
## 505 list(level1 = 5, level2 = 60) 2 1 4 0
## 506 list(level1 = 5, level2 = 60) 2 1 0 1
## 507 list(level1 = 5, level2 = 60) 2 1 1 1
## 508 list(level1 = 5, level2 = 60) 2 1 2 1
## 509 list(level1 = 5, level2 = 60) 2 1 3 1
## 510 list(level1 = 5, level2 = 60) 2 1 4 1
## 511 list(level1 = 5, level2 = 60) 2 1 0 0
## 512 list(level1 = 5, level2 = 60) 2 1 1 0
## 513 list(level1 = 5, level2 = 60) 2 1 2 0
## 514 list(level1 = 5, level2 = 60) 2 1 3 0
## 515 list(level1 = 5, level2 = 60) 2 1 4 0
## 516 list(level1 = 5, level2 = 60) 2 1 0 1
## 517 list(level1 = 5, level2 = 60) 2 1 1 1
## 518 list(level1 = 5, level2 = 60) 2 1 2 1
## 519 list(level1 = 5, level2 = 60) 2 1 3 1
## 520 list(level1 = 5, level2 = 60) 2 1 4 1
## 521 list(level1 = 5, level2 = 60) 2 1 0 0
## 522 list(level1 = 5, level2 = 60) 2 1 1 0
## 523 list(level1 = 5, level2 = 60) 2 1 2 0
## 524 list(level1 = 5, level2 = 60) 2 1 3 0
## 525 list(level1 = 5, level2 = 60) 2 1 4 0
## 526 list(level1 = 5, level2 = 60) 2 1 0 1
## 527 list(level1 = 5, level2 = 60) 2 1 1 1
## 528 list(level1 = 5, level2 = 60) 2 1 2 1
## 529 list(level1 = 5, level2 = 60) 2 1 3 1
## 530 list(level1 = 5, level2 = 60) 2 1 4 1
## 531 list(level1 = 5, level2 = 60) 2 1 0 1
## 532 list(level1 = 5, level2 = 60) 2 1 1 1
## 533 list(level1 = 5, level2 = 60) 2 1 2 1
## 534 list(level1 = 5, level2 = 60) 2 1 3 1
## 535 list(level1 = 5, level2 = 60) 2 1 4 1
## 536 list(level1 = 5, level2 = 60) 2 1 0 1
## 537 list(level1 = 5, level2 = 60) 2 1 1 1
## 538 list(level1 = 5, level2 = 60) 2 1 2 1
## 539 list(level1 = 5, level2 = 60) 2 1 3 1
## 540 list(level1 = 5, level2 = 60) 2 1 4 1
## 541 list(level1 = 5, level2 = 60) 2 1 0 1
## 542 list(level1 = 5, level2 = 60) 2 1 1 1
## 543 list(level1 = 5, level2 = 60) 2 1 2 1
## 544 list(level1 = 5, level2 = 60) 2 1 3 1
## 545 list(level1 = 5, level2 = 60) 2 1 4 1
## 546 list(level1 = 5, level2 = 60) 2 1 0 0
## 547 list(level1 = 5, level2 = 60) 2 1 1 0
## 548 list(level1 = 5, level2 = 60) 2 1 2 0
## 549 list(level1 = 5, level2 = 60) 2 1 3 0
## 550 list(level1 = 5, level2 = 60) 2 1 4 0
## 551 list(level1 = 5, level2 = 60) 2 1 0 0
## 552 list(level1 = 5, level2 = 60) 2 1 1 0
## 553 list(level1 = 5, level2 = 60) 2 1 2 0
## 554 list(level1 = 5, level2 = 60) 2 1 3 0
## 555 list(level1 = 5, level2 = 60) 2 1 4 0
## 556 list(level1 = 5, level2 = 60) 2 1 0 0
## 557 list(level1 = 5, level2 = 60) 2 1 1 0
## 558 list(level1 = 5, level2 = 60) 2 1 2 0
## 559 list(level1 = 5, level2 = 60) 2 1 3 0
## 560 list(level1 = 5, level2 = 60) 2 1 4 0
## 561 list(level1 = 5, level2 = 60) 2 1 0 1
## 562 list(level1 = 5, level2 = 60) 2 1 1 1
## 563 list(level1 = 5, level2 = 60) 2 1 2 1
## 564 list(level1 = 5, level2 = 60) 2 1 3 1
## 565 list(level1 = 5, level2 = 60) 2 1 4 1
## 566 list(level1 = 5, level2 = 60) 2 1 0 1
## 567 list(level1 = 5, level2 = 60) 2 1 1 1
## 568 list(level1 = 5, level2 = 60) 2 1 2 1
## 569 list(level1 = 5, level2 = 60) 2 1 3 1
## 570 list(level1 = 5, level2 = 60) 2 1 4 1
## 571 list(level1 = 5, level2 = 60) 2 1 0 1
## 572 list(level1 = 5, level2 = 60) 2 1 1 1
## 573 list(level1 = 5, level2 = 60) 2 1 2 1
## 574 list(level1 = 5, level2 = 60) 2 1 3 1
## 575 list(level1 = 5, level2 = 60) 2 1 4 1
## 576 list(level1 = 5, level2 = 60) 2 1 0 0
## 577 list(level1 = 5, level2 = 60) 2 1 1 0
## 578 list(level1 = 5, level2 = 60) 2 1 2 0
## 579 list(level1 = 5, level2 = 60) 2 1 3 0
## 580 list(level1 = 5, level2 = 60) 2 1 4 0
## 581 list(level1 = 5, level2 = 60) 2 1 0 0
## 582 list(level1 = 5, level2 = 60) 2 1 1 0
## 583 list(level1 = 5, level2 = 60) 2 1 2 0
## 584 list(level1 = 5, level2 = 60) 2 1 3 0
## 585 list(level1 = 5, level2 = 60) 2 1 4 0
## 586 list(level1 = 5, level2 = 60) 2 1 0 0
## 587 list(level1 = 5, level2 = 60) 2 1 1 0
## 588 list(level1 = 5, level2 = 60) 2 1 2 0
## 589 list(level1 = 5, level2 = 60) 2 1 3 0
## 590 list(level1 = 5, level2 = 60) 2 1 4 0
## 591 list(level1 = 5, level2 = 60) 2 1 0 1
## 592 list(level1 = 5, level2 = 60) 2 1 1 1
## 593 list(level1 = 5, level2 = 60) 2 1 2 1
## 594 list(level1 = 5, level2 = 60) 2 1 3 1
## 595 list(level1 = 5, level2 = 60) 2 1 4 1
## 596 list(level1 = 5, level2 = 60) 2 1 0 1
## 597 list(level1 = 5, level2 = 60) 2 1 1 1
## 598 list(level1 = 5, level2 = 60) 2 1 2 1
## 599 list(level1 = 5, level2 = 60) 2 1 3 1
## 600 list(level1 = 5, level2 = 60) 2 1 4 1
## 601 list(level1 = 5, level2 = 60) 3 1 0 1
## 602 list(level1 = 5, level2 = 60) 3 1 1 1
## 603 list(level1 = 5, level2 = 60) 3 1 2 1
## 604 list(level1 = 5, level2 = 60) 3 1 3 1
## 605 list(level1 = 5, level2 = 60) 3 1 4 1
## 606 list(level1 = 5, level2 = 60) 3 1 0 1
## 607 list(level1 = 5, level2 = 60) 3 1 1 1
## 608 list(level1 = 5, level2 = 60) 3 1 2 1
## 609 list(level1 = 5, level2 = 60) 3 1 3 1
## 610 list(level1 = 5, level2 = 60) 3 1 4 1
## 611 list(level1 = 5, level2 = 60) 3 1 0 1
## 612 list(level1 = 5, level2 = 60) 3 1 1 1
## 613 list(level1 = 5, level2 = 60) 3 1 2 1
## 614 list(level1 = 5, level2 = 60) 3 1 3 1
## 615 list(level1 = 5, level2 = 60) 3 1 4 1
## 616 list(level1 = 5, level2 = 60) 3 1 0 0
## 617 list(level1 = 5, level2 = 60) 3 1 1 0
## 618 list(level1 = 5, level2 = 60) 3 1 2 0
## 619 list(level1 = 5, level2 = 60) 3 1 3 0
## 620 list(level1 = 5, level2 = 60) 3 1 4 0
## 621 list(level1 = 5, level2 = 60) 3 1 0 0
## 622 list(level1 = 5, level2 = 60) 3 1 1 0
## 623 list(level1 = 5, level2 = 60) 3 1 2 0
## 624 list(level1 = 5, level2 = 60) 3 1 3 0
## 625 list(level1 = 5, level2 = 60) 3 1 4 0
## 626 list(level1 = 5, level2 = 60) 3 1 0 0
## 627 list(level1 = 5, level2 = 60) 3 1 1 0
## 628 list(level1 = 5, level2 = 60) 3 1 2 0
## 629 list(level1 = 5, level2 = 60) 3 1 3 0
## 630 list(level1 = 5, level2 = 60) 3 1 4 0
## 631 list(level1 = 5, level2 = 60) 3 1 0 1
## 632 list(level1 = 5, level2 = 60) 3 1 1 1
## 633 list(level1 = 5, level2 = 60) 3 1 2 1
## 634 list(level1 = 5, level2 = 60) 3 1 3 1
## 635 list(level1 = 5, level2 = 60) 3 1 4 1
## 636 list(level1 = 5, level2 = 60) 3 1 0 0
## 637 list(level1 = 5, level2 = 60) 3 1 1 0
## 638 list(level1 = 5, level2 = 60) 3 1 2 0
## 639 list(level1 = 5, level2 = 60) 3 1 3 0
## 640 list(level1 = 5, level2 = 60) 3 1 4 0
## 641 list(level1 = 5, level2 = 60) 3 1 0 1
## 642 list(level1 = 5, level2 = 60) 3 1 1 1
## 643 list(level1 = 5, level2 = 60) 3 1 2 1
## 644 list(level1 = 5, level2 = 60) 3 1 3 1
## 645 list(level1 = 5, level2 = 60) 3 1 4 1
## 646 list(level1 = 5, level2 = 60) 3 1 0 0
## 647 list(level1 = 5, level2 = 60) 3 1 1 0
## 648 list(level1 = 5, level2 = 60) 3 1 2 0
## 649 list(level1 = 5, level2 = 60) 3 1 3 0
## 650 list(level1 = 5, level2 = 60) 3 1 4 0
## 651 list(level1 = 5, level2 = 60) 3 1 0 0
## 652 list(level1 = 5, level2 = 60) 3 1 1 0
## 653 list(level1 = 5, level2 = 60) 3 1 2 0
## 654 list(level1 = 5, level2 = 60) 3 1 3 0
## 655 list(level1 = 5, level2 = 60) 3 1 4 0
## 656 list(level1 = 5, level2 = 60) 3 1 0 1
## 657 list(level1 = 5, level2 = 60) 3 1 1 1
## 658 list(level1 = 5, level2 = 60) 3 1 2 1
## 659 list(level1 = 5, level2 = 60) 3 1 3 1
## 660 list(level1 = 5, level2 = 60) 3 1 4 1
## 661 list(level1 = 5, level2 = 60) 3 1 0 1
## 662 list(level1 = 5, level2 = 60) 3 1 1 1
## 663 list(level1 = 5, level2 = 60) 3 1 2 1
## 664 list(level1 = 5, level2 = 60) 3 1 3 1
## 665 list(level1 = 5, level2 = 60) 3 1 4 1
## 666 list(level1 = 5, level2 = 60) 3 1 0 1
## 667 list(level1 = 5, level2 = 60) 3 1 1 1
## 668 list(level1 = 5, level2 = 60) 3 1 2 1
## 669 list(level1 = 5, level2 = 60) 3 1 3 1
## 670 list(level1 = 5, level2 = 60) 3 1 4 1
## 671 list(level1 = 5, level2 = 60) 3 1 0 1
## 672 list(level1 = 5, level2 = 60) 3 1 1 1
## 673 list(level1 = 5, level2 = 60) 3 1 2 1
## 674 list(level1 = 5, level2 = 60) 3 1 3 1
## 675 list(level1 = 5, level2 = 60) 3 1 4 1
## 676 list(level1 = 5, level2 = 60) 3 1 0 0
## 677 list(level1 = 5, level2 = 60) 3 1 1 0
## 678 list(level1 = 5, level2 = 60) 3 1 2 0
## 679 list(level1 = 5, level2 = 60) 3 1 3 0
## 680 list(level1 = 5, level2 = 60) 3 1 4 0
## 681 list(level1 = 5, level2 = 60) 3 1 0 1
## 682 list(level1 = 5, level2 = 60) 3 1 1 1
## 683 list(level1 = 5, level2 = 60) 3 1 2 1
## 684 list(level1 = 5, level2 = 60) 3 1 3 1
## 685 list(level1 = 5, level2 = 60) 3 1 4 1
## 686 list(level1 = 5, level2 = 60) 3 1 0 1
## 687 list(level1 = 5, level2 = 60) 3 1 1 1
## 688 list(level1 = 5, level2 = 60) 3 1 2 1
## 689 list(level1 = 5, level2 = 60) 3 1 3 1
## 690 list(level1 = 5, level2 = 60) 3 1 4 1
## 691 list(level1 = 5, level2 = 60) 3 1 0 0
## 692 list(level1 = 5, level2 = 60) 3 1 1 0
## 693 list(level1 = 5, level2 = 60) 3 1 2 0
## 694 list(level1 = 5, level2 = 60) 3 1 3 0
## 695 list(level1 = 5, level2 = 60) 3 1 4 0
## 696 list(level1 = 5, level2 = 60) 3 1 0 1
## 697 list(level1 = 5, level2 = 60) 3 1 1 1
## 698 list(level1 = 5, level2 = 60) 3 1 2 1
## 699 list(level1 = 5, level2 = 60) 3 1 3 1
## 700 list(level1 = 5, level2 = 60) 3 1 4 1
## 701 list(level1 = 5, level2 = 60) 3 1 0 1
## 702 list(level1 = 5, level2 = 60) 3 1 1 1
## 703 list(level1 = 5, level2 = 60) 3 1 2 1
## 704 list(level1 = 5, level2 = 60) 3 1 3 1
## 705 list(level1 = 5, level2 = 60) 3 1 4 1
## 706 list(level1 = 5, level2 = 60) 3 1 0 1
## 707 list(level1 = 5, level2 = 60) 3 1 1 1
## 708 list(level1 = 5, level2 = 60) 3 1 2 1
## 709 list(level1 = 5, level2 = 60) 3 1 3 1
## 710 list(level1 = 5, level2 = 60) 3 1 4 1
## 711 list(level1 = 5, level2 = 60) 3 1 0 0
## 712 list(level1 = 5, level2 = 60) 3 1 1 0
## 713 list(level1 = 5, level2 = 60) 3 1 2 0
## 714 list(level1 = 5, level2 = 60) 3 1 3 0
## 715 list(level1 = 5, level2 = 60) 3 1 4 0
## 716 list(level1 = 5, level2 = 60) 3 1 0 0
## 717 list(level1 = 5, level2 = 60) 3 1 1 0
## 718 list(level1 = 5, level2 = 60) 3 1 2 0
## 719 list(level1 = 5, level2 = 60) 3 1 3 0
## 720 list(level1 = 5, level2 = 60) 3 1 4 0
## 721 list(level1 = 5, level2 = 60) 3 1 0 1
## 722 list(level1 = 5, level2 = 60) 3 1 1 1
## 723 list(level1 = 5, level2 = 60) 3 1 2 1
## 724 list(level1 = 5, level2 = 60) 3 1 3 1
## 725 list(level1 = 5, level2 = 60) 3 1 4 1
## 726 list(level1 = 5, level2 = 60) 3 1 0 1
## 727 list(level1 = 5, level2 = 60) 3 1 1 1
## 728 list(level1 = 5, level2 = 60) 3 1 2 1
## 729 list(level1 = 5, level2 = 60) 3 1 3 1
## 730 list(level1 = 5, level2 = 60) 3 1 4 1
## 731 list(level1 = 5, level2 = 60) 3 1 0 1
## 732 list(level1 = 5, level2 = 60) 3 1 1 1
## 733 list(level1 = 5, level2 = 60) 3 1 2 1
## 734 list(level1 = 5, level2 = 60) 3 1 3 1
## 735 list(level1 = 5, level2 = 60) 3 1 4 1
## 736 list(level1 = 5, level2 = 60) 3 1 0 0
## 737 list(level1 = 5, level2 = 60) 3 1 1 0
## 738 list(level1 = 5, level2 = 60) 3 1 2 0
## 739 list(level1 = 5, level2 = 60) 3 1 3 0
## 740 list(level1 = 5, level2 = 60) 3 1 4 0
## 741 list(level1 = 5, level2 = 60) 3 1 0 1
## 742 list(level1 = 5, level2 = 60) 3 1 1 1
## 743 list(level1 = 5, level2 = 60) 3 1 2 1
## 744 list(level1 = 5, level2 = 60) 3 1 3 1
## 745 list(level1 = 5, level2 = 60) 3 1 4 1
## 746 list(level1 = 5, level2 = 60) 3 1 0 0
## 747 list(level1 = 5, level2 = 60) 3 1 1 0
## 748 list(level1 = 5, level2 = 60) 3 1 2 0
## 749 list(level1 = 5, level2 = 60) 3 1 3 0
## 750 list(level1 = 5, level2 = 60) 3 1 4 0
## 751 list(level1 = 5, level2 = 60) 3 1 0 0
## 752 list(level1 = 5, level2 = 60) 3 1 1 0
## 753 list(level1 = 5, level2 = 60) 3 1 2 0
## 754 list(level1 = 5, level2 = 60) 3 1 3 0
## 755 list(level1 = 5, level2 = 60) 3 1 4 0
## 756 list(level1 = 5, level2 = 60) 3 1 0 1
## 757 list(level1 = 5, level2 = 60) 3 1 1 1
## 758 list(level1 = 5, level2 = 60) 3 1 2 1
## 759 list(level1 = 5, level2 = 60) 3 1 3 1
## 760 list(level1 = 5, level2 = 60) 3 1 4 1
## 761 list(level1 = 5, level2 = 60) 3 1 0 0
## 762 list(level1 = 5, level2 = 60) 3 1 1 0
## 763 list(level1 = 5, level2 = 60) 3 1 2 0
## 764 list(level1 = 5, level2 = 60) 3 1 3 0
## 765 list(level1 = 5, level2 = 60) 3 1 4 0
## 766 list(level1 = 5, level2 = 60) 3 1 0 1
## 767 list(level1 = 5, level2 = 60) 3 1 1 1
## 768 list(level1 = 5, level2 = 60) 3 1 2 1
## 769 list(level1 = 5, level2 = 60) 3 1 3 1
## 770 list(level1 = 5, level2 = 60) 3 1 4 1
## 771 list(level1 = 5, level2 = 60) 3 1 0 0
## 772 list(level1 = 5, level2 = 60) 3 1 1 0
## 773 list(level1 = 5, level2 = 60) 3 1 2 0
## 774 list(level1 = 5, level2 = 60) 3 1 3 0
## 775 list(level1 = 5, level2 = 60) 3 1 4 0
## 776 list(level1 = 5, level2 = 60) 3 1 0 1
## 777 list(level1 = 5, level2 = 60) 3 1 1 1
## 778 list(level1 = 5, level2 = 60) 3 1 2 1
## 779 list(level1 = 5, level2 = 60) 3 1 3 1
## 780 list(level1 = 5, level2 = 60) 3 1 4 1
## 781 list(level1 = 5, level2 = 60) 3 1 0 0
## 782 list(level1 = 5, level2 = 60) 3 1 1 0
## 783 list(level1 = 5, level2 = 60) 3 1 2 0
## 784 list(level1 = 5, level2 = 60) 3 1 3 0
## 785 list(level1 = 5, level2 = 60) 3 1 4 0
## 786 list(level1 = 5, level2 = 60) 3 1 0 1
## 787 list(level1 = 5, level2 = 60) 3 1 1 1
## 788 list(level1 = 5, level2 = 60) 3 1 2 1
## 789 list(level1 = 5, level2 = 60) 3 1 3 1
## 790 list(level1 = 5, level2 = 60) 3 1 4 1
## 791 list(level1 = 5, level2 = 60) 3 1 0 0
## 792 list(level1 = 5, level2 = 60) 3 1 1 0
## 793 list(level1 = 5, level2 = 60) 3 1 2 0
## 794 list(level1 = 5, level2 = 60) 3 1 3 0
## 795 list(level1 = 5, level2 = 60) 3 1 4 0
## 796 list(level1 = 5, level2 = 60) 3 1 0 0
## 797 list(level1 = 5, level2 = 60) 3 1 1 0
## 798 list(level1 = 5, level2 = 60) 3 1 2 0
## 799 list(level1 = 5, level2 = 60) 3 1 3 0
## 800 list(level1 = 5, level2 = 60) 3 1 4 0
## 801 list(level1 = 5, level2 = 60) 3 1 0 0
## 802 list(level1 = 5, level2 = 60) 3 1 1 0
## 803 list(level1 = 5, level2 = 60) 3 1 2 0
## 804 list(level1 = 5, level2 = 60) 3 1 3 0
## 805 list(level1 = 5, level2 = 60) 3 1 4 0
## 806 list(level1 = 5, level2 = 60) 3 1 0 1
## 807 list(level1 = 5, level2 = 60) 3 1 1 1
## 808 list(level1 = 5, level2 = 60) 3 1 2 1
## 809 list(level1 = 5, level2 = 60) 3 1 3 1
## 810 list(level1 = 5, level2 = 60) 3 1 4 1
## 811 list(level1 = 5, level2 = 60) 3 1 0 1
## 812 list(level1 = 5, level2 = 60) 3 1 1 1
## 813 list(level1 = 5, level2 = 60) 3 1 2 1
## 814 list(level1 = 5, level2 = 60) 3 1 3 1
## 815 list(level1 = 5, level2 = 60) 3 1 4 1
## 816 list(level1 = 5, level2 = 60) 3 1 0 1
## 817 list(level1 = 5, level2 = 60) 3 1 1 1
## 818 list(level1 = 5, level2 = 60) 3 1 2 1
## 819 list(level1 = 5, level2 = 60) 3 1 3 1
## 820 list(level1 = 5, level2 = 60) 3 1 4 1
## 821 list(level1 = 5, level2 = 60) 3 1 0 0
## 822 list(level1 = 5, level2 = 60) 3 1 1 0
## 823 list(level1 = 5, level2 = 60) 3 1 2 0
## 824 list(level1 = 5, level2 = 60) 3 1 3 0
## 825 list(level1 = 5, level2 = 60) 3 1 4 0
## 826 list(level1 = 5, level2 = 60) 3 1 0 1
## 827 list(level1 = 5, level2 = 60) 3 1 1 1
## 828 list(level1 = 5, level2 = 60) 3 1 2 1
## 829 list(level1 = 5, level2 = 60) 3 1 3 1
## 830 list(level1 = 5, level2 = 60) 3 1 4 1
## 831 list(level1 = 5, level2 = 60) 3 1 0 1
## 832 list(level1 = 5, level2 = 60) 3 1 1 1
## 833 list(level1 = 5, level2 = 60) 3 1 2 1
## 834 list(level1 = 5, level2 = 60) 3 1 3 1
## 835 list(level1 = 5, level2 = 60) 3 1 4 1
## 836 list(level1 = 5, level2 = 60) 3 1 0 0
## 837 list(level1 = 5, level2 = 60) 3 1 1 0
## 838 list(level1 = 5, level2 = 60) 3 1 2 0
## 839 list(level1 = 5, level2 = 60) 3 1 3 0
## 840 list(level1 = 5, level2 = 60) 3 1 4 0
## 841 list(level1 = 5, level2 = 60) 3 1 0 1
## 842 list(level1 = 5, level2 = 60) 3 1 1 1
## 843 list(level1 = 5, level2 = 60) 3 1 2 1
## 844 list(level1 = 5, level2 = 60) 3 1 3 1
## 845 list(level1 = 5, level2 = 60) 3 1 4 1
## 846 list(level1 = 5, level2 = 60) 3 1 0 1
## 847 list(level1 = 5, level2 = 60) 3 1 1 1
## 848 list(level1 = 5, level2 = 60) 3 1 2 1
## 849 list(level1 = 5, level2 = 60) 3 1 3 1
## 850 list(level1 = 5, level2 = 60) 3 1 4 1
## 851 list(level1 = 5, level2 = 60) 3 1 0 1
## 852 list(level1 = 5, level2 = 60) 3 1 1 1
## 853 list(level1 = 5, level2 = 60) 3 1 2 1
## 854 list(level1 = 5, level2 = 60) 3 1 3 1
## 855 list(level1 = 5, level2 = 60) 3 1 4 1
## 856 list(level1 = 5, level2 = 60) 3 1 0 1
## 857 list(level1 = 5, level2 = 60) 3 1 1 1
## 858 list(level1 = 5, level2 = 60) 3 1 2 1
## 859 list(level1 = 5, level2 = 60) 3 1 3 1
## 860 list(level1 = 5, level2 = 60) 3 1 4 1
## 861 list(level1 = 5, level2 = 60) 3 1 0 0
## 862 list(level1 = 5, level2 = 60) 3 1 1 0
## 863 list(level1 = 5, level2 = 60) 3 1 2 0
## 864 list(level1 = 5, level2 = 60) 3 1 3 0
## 865 list(level1 = 5, level2 = 60) 3 1 4 0
## 866 list(level1 = 5, level2 = 60) 3 1 0 1
## 867 list(level1 = 5, level2 = 60) 3 1 1 1
## 868 list(level1 = 5, level2 = 60) 3 1 2 1
## 869 list(level1 = 5, level2 = 60) 3 1 3 1
## 870 list(level1 = 5, level2 = 60) 3 1 4 1
## 871 list(level1 = 5, level2 = 60) 3 1 0 0
## 872 list(level1 = 5, level2 = 60) 3 1 1 0
## 873 list(level1 = 5, level2 = 60) 3 1 2 0
## 874 list(level1 = 5, level2 = 60) 3 1 3 0
## 875 list(level1 = 5, level2 = 60) 3 1 4 0
## 876 list(level1 = 5, level2 = 60) 3 1 0 0
## 877 list(level1 = 5, level2 = 60) 3 1 1 0
## 878 list(level1 = 5, level2 = 60) 3 1 2 0
## 879 list(level1 = 5, level2 = 60) 3 1 3 0
## 880 list(level1 = 5, level2 = 60) 3 1 4 0
## 881 list(level1 = 5, level2 = 60) 3 1 0 1
## 882 list(level1 = 5, level2 = 60) 3 1 1 1
## 883 list(level1 = 5, level2 = 60) 3 1 2 1
## 884 list(level1 = 5, level2 = 60) 3 1 3 1
## 885 list(level1 = 5, level2 = 60) 3 1 4 1
## 886 list(level1 = 5, level2 = 60) 3 1 0 1
## 887 list(level1 = 5, level2 = 60) 3 1 1 1
## 888 list(level1 = 5, level2 = 60) 3 1 2 1
## 889 list(level1 = 5, level2 = 60) 3 1 3 1
## 890 list(level1 = 5, level2 = 60) 3 1 4 1
## 891 list(level1 = 5, level2 = 60) 3 1 0 0
## 892 list(level1 = 5, level2 = 60) 3 1 1 0
## 893 list(level1 = 5, level2 = 60) 3 1 2 0
## 894 list(level1 = 5, level2 = 60) 3 1 3 0
## 895 list(level1 = 5, level2 = 60) 3 1 4 0
## 896 list(level1 = 5, level2 = 60) 3 1 0 1
## 897 list(level1 = 5, level2 = 60) 3 1 1 1
## 898 list(level1 = 5, level2 = 60) 3 1 2 1
## 899 list(level1 = 5, level2 = 60) 3 1 3 1
## 900 list(level1 = 5, level2 = 60) 3 1 4 1
## time.trt_1 trt level1_id individual error int_clust
## 1 0 Placebo 1 1 0.6740936510 -0.0749605335
## 2 1 Placebo 2 1 0.0238153323 -0.0749605335
## 3 2 Placebo 3 1 1.3606860744 -0.0749605335
## 4 3 Placebo 4 1 -0.9357649363 -0.0749605335
## 5 4 Placebo 5 1 2.3290412213 -0.0749605335
## 6 0 Drug 1 2 0.3406028961 -0.3988322796
## 7 0 Drug 2 2 0.7251773934 -0.3988322796
## 8 0 Drug 3 2 -0.9608824995 -0.3988322796
## 9 0 Drug 4 2 -1.2071283391 -0.3988322796
## 10 0 Drug 5 2 0.2721456721 -0.3988322796
## 11 0 Placebo 1 3 0.3579318648 -0.5635761480
## 12 1 Placebo 2 3 -1.1942107753 -0.5635761480
## 13 2 Placebo 3 3 -0.0567875941 -0.5635761480
## 14 3 Placebo 4 3 0.7309923470 -0.5635761480
## 15 4 Placebo 5 3 -0.0493149591 -0.5635761480
## 16 0 Drug 1 4 -0.6617086115 0.4010985233
## 17 0 Drug 2 4 0.8848552667 0.4010985233
## 18 0 Drug 3 4 -1.1005066625 0.4010985233
## 19 0 Drug 4 4 -0.6454670778 0.4010985233
## 20 0 Drug 5 4 1.1736951887 0.4010985233
## 21 0 Drug 1 5 0.4705455157 0.1941890939
## 22 0 Drug 2 5 -0.2054537747 0.1941890939
## 23 0 Drug 3 5 0.4068527063 0.1941890939
## 24 0 Drug 4 5 -0.0044732052 0.1941890939
## 25 0 Drug 5 5 0.7049747266 0.1941890939
## 26 0 Placebo 1 6 0.6741629262 -0.4969617196
## 27 1 Placebo 2 6 0.2018808194 -0.4969617196
## 28 2 Placebo 3 6 0.7542950903 -0.4969617196
## 29 3 Placebo 4 6 -0.6907320154 -0.4969617196
## 30 4 Placebo 5 6 -0.6957348854 -0.4969617196
## 31 0 Placebo 1 7 0.3792205045 0.0505374684
## 32 1 Placebo 2 7 0.1475417024 0.0505374684
## 33 2 Placebo 3 7 -0.5330567071 0.0505374684
## 34 3 Placebo 4 7 -0.6403922585 0.0505374684
## 35 4 Placebo 5 7 -0.4048865820 0.0505374684
## 36 0 Drug 1 8 -1.2717183265 0.4462609800
## 37 0 Drug 2 8 -0.4592944251 0.4462609800
## 38 0 Drug 3 8 -1.3157429650 0.4462609800
## 39 0 Drug 4 8 1.0846039307 0.4462609800
## 40 0 Drug 5 8 1.1954658128 0.4462609800
## 41 0 Placebo 1 9 -1.4858156429 -0.2476761974
## 42 1 Placebo 2 9 0.8451039578 -0.2476761974
## 43 2 Placebo 3 9 2.5744625778 -0.2476761974
## 44 3 Placebo 4 9 -0.3945686607 -0.2476761974
## 45 4 Placebo 5 9 -0.4127099043 -0.2476761974
## 46 0 Drug 1 10 -0.2155464355 -0.1913466236
## 47 0 Drug 2 10 -0.5560456221 -0.1913466236
## 48 0 Drug 3 10 0.8191193445 -0.1913466236
## 49 0 Drug 4 10 -0.6485905372 -0.1913466236
## 50 0 Drug 5 10 1.0780367640 -0.1913466236
## 51 0 Placebo 1 11 -0.6044341522 0.4963496675
## 52 1 Placebo 2 11 0.5285346975 0.4963496675
## 53 2 Placebo 3 11 0.3765376802 0.4963496675
## 54 3 Placebo 4 11 -0.1338196843 0.4963496675
## 55 4 Placebo 5 11 -1.5522240545 0.4963496675
## 56 0 Placebo 1 12 -0.4668161114 0.1499713786
## 57 1 Placebo 2 12 0.3812162085 0.1499713786
## 58 2 Placebo 3 12 0.0620653206 0.1499713786
## 59 3 Placebo 4 12 0.4082082288 0.1499713786
## 60 4 Placebo 5 12 0.5636052763 0.1499713786
## 61 0 Placebo 1 13 -0.1556640147 0.2771079919
## 62 1 Placebo 2 13 0.2396231908 0.2771079919
## 63 2 Placebo 3 13 -0.7614081259 0.2771079919
## 64 3 Placebo 4 13 0.2743731319 0.2771079919
## 65 4 Placebo 5 13 1.5111497066 0.2771079919
## 66 0 Drug 1 14 -0.3845834688 -0.4194741137
## 67 0 Drug 2 14 -1.4568825798 -0.4194741137
## 68 0 Drug 3 14 -0.8870066311 -0.4194741137
## 69 0 Drug 4 14 -0.5694694710 -0.4194741137
## 70 0 Drug 5 14 1.3701477177 -0.4194741137
## 71 0 Drug 1 15 0.2409122975 -0.0497613747
## 72 0 Drug 2 15 -0.1600908802 -0.0497613747
## 73 0 Drug 3 15 -0.0069321202 -0.0497613747
## 74 0 Drug 4 15 -0.6214630416 -0.0497613747
## 75 0 Drug 5 15 -0.5894854398 -0.0497613747
## 76 0 Drug 1 16 1.3071803839 0.0120742941
## 77 0 Drug 2 16 1.3480139411 0.0120742941
## 78 0 Drug 3 16 0.2385926184 0.0120742941
## 79 0 Drug 4 16 -0.2253242494 0.0120742941
## 80 0 Drug 5 16 1.1909070244 0.0120742941
## 81 0 Placebo 1 17 -1.0874534068 -0.0504042583
## 82 1 Placebo 2 17 -0.2990555734 -0.0504042583
## 83 2 Placebo 3 17 -0.7404755348 -0.0504042583
## 84 3 Placebo 4 17 -0.6300447820 -0.0504042583
## 85 4 Placebo 5 17 0.8566543940 -0.0504042583
## 86 0 Placebo 1 18 -0.9820959363 -0.5774882278
## 87 1 Placebo 2 18 -0.1532040545 -0.5774882278
## 88 2 Placebo 3 18 -0.0973581318 -0.5774882278
## 89 3 Placebo 4 18 0.3044590298 -0.5774882278
## 90 4 Placebo 5 18 -0.5839273948 -0.5774882278
## 91 0 Drug 1 19 0.4340874475 -0.2570927557
## 92 0 Drug 2 19 -0.7610820927 -0.2570927557
## 93 0 Drug 3 19 0.1692806001 -0.2570927557
## 94 0 Drug 4 19 0.3309873927 -0.2570927557
## 95 0 Drug 5 19 -0.3997561862 -0.2570927557
## 96 0 Placebo 1 20 0.6131662370 -0.0147902241
## 97 1 Placebo 2 20 1.1547279163 -0.0147902241
## 98 2 Placebo 3 20 2.0169408666 -0.0147902241
## 99 3 Placebo 4 20 0.1770204645 -0.0147902241
## 100 4 Placebo 5 20 -0.6200389600 -0.0147902241
## 101 0 Drug 1 21 0.6539129668 0.1081007956
## 102 0 Drug 2 21 1.1768509563 0.1081007956
## 103 0 Drug 3 21 -0.5173768716 0.1081007956
## 104 0 Drug 4 21 0.7180001433 0.1081007956
## 105 0 Drug 5 21 0.7169831824 0.1081007956
## 106 0 Drug 1 22 0.3116551960 0.2760023029
## 107 0 Drug 2 22 1.2606587463 0.2760023029
## 108 0 Drug 3 22 0.5929694980 0.2760023029
## 109 0 Drug 4 22 -0.8238491277 0.2760023029
## 110 0 Drug 5 22 -0.7641633538 0.2760023029
## 111 0 Placebo 1 23 0.3736134471 -0.2618105207
## 112 1 Placebo 2 23 -1.2676441480 -0.2618105207
## 113 2 Placebo 3 23 0.8941727215 -0.2618105207
## 114 3 Placebo 4 23 -2.2989263028 -0.2618105207
## 115 4 Placebo 5 23 -0.6149818649 -0.2618105207
## 116 0 Drug 1 24 -1.3303892741 -0.3130835595
## 117 0 Drug 2 24 0.1258322273 -0.3130835595
## 118 0 Drug 3 24 1.0233280545 -0.3130835595
## 119 0 Drug 4 24 0.2353467690 -0.3130835595
## 120 0 Drug 5 24 0.2388324540 -0.3130835595
## 121 0 Placebo 1 25 -0.6438885228 0.5504060007
## 122 1 Placebo 2 25 0.9662672629 0.5504060007
## 123 2 Placebo 3 25 -0.2397638164 0.5504060007
## 124 3 Placebo 4 25 -0.1050037850 0.5504060007
## 125 4 Placebo 5 25 -0.8640998937 0.5504060007
## 126 0 Drug 1 26 -0.9158207263 0.1443355633
## 127 0 Drug 2 26 0.0488325290 0.1443355633
## 128 0 Drug 3 26 1.7511959983 0.1443355633
## 129 0 Drug 4 26 0.1732427464 0.1443355633
## 130 0 Drug 5 26 -1.0904884209 0.1443355633
## 131 0 Placebo 1 27 -0.4516754787 0.4295187564
## 132 1 Placebo 2 27 0.3587954738 0.4295187564
## 133 2 Placebo 3 27 1.3268712633 0.4295187564
## 134 3 Placebo 4 27 1.5102633431 0.4295187564
## 135 4 Placebo 5 27 1.1822907933 0.4295187564
## 136 0 Drug 1 28 0.9311252078 0.4105793692
## 137 0 Drug 2 28 -0.8473261062 0.4105793692
## 138 0 Drug 3 28 -0.7066035474 0.4105793692
## 139 0 Drug 4 28 -0.4433879608 0.4105793692
## 140 0 Drug 5 28 1.3133140534 0.4105793692
## 141 0 Drug 1 29 1.1081666146 -0.4682032871
## 142 0 Drug 2 29 0.1613322072 -0.4682032871
## 143 0 Drug 3 29 0.2217919718 -0.4682032871
## 144 0 Drug 4 29 0.2324923530 -0.4682032871
## 145 0 Drug 5 29 -1.4510015832 -0.4682032871
## 146 0 Placebo 1 30 3.0001640633 -0.0876660750
## 147 1 Placebo 2 30 -0.8977796747 -0.0876660750
## 148 2 Placebo 3 30 -0.9819781870 -0.0876660750
## 149 3 Placebo 4 30 -2.2383926969 -0.0876660750
## 150 4 Placebo 5 30 1.0030943923 -0.0876660750
## 151 0 Placebo 1 31 0.7255993315 0.4169706024
## 152 1 Placebo 2 31 -0.8924272549 0.4169706024
## 153 2 Placebo 3 31 -0.0876486334 0.4169706024
## 154 3 Placebo 4 31 0.8713175852 0.4169706024
## 155 4 Placebo 5 31 -1.1733741308 0.4169706024
## 156 0 Drug 1 32 0.8404106686 0.2889382421
## 157 0 Drug 2 32 0.0077954598 0.2889382421
## 158 0 Drug 3 32 -0.1790383619 0.2889382421
## 159 0 Drug 4 32 0.1309608374 0.2889382421
## 160 0 Drug 5 32 1.1771976100 0.2889382421
## 161 0 Drug 1 33 -1.4992536434 -0.1496469536
## 162 0 Drug 2 33 0.8083291747 -0.1496469536
## 163 0 Drug 3 33 -0.5056899962 -0.1496469536
## 164 0 Drug 4 33 0.0233686826 -0.1496469536
## 165 0 Drug 5 33 -1.3184013745 -0.1496469536
## 166 0 Drug 1 34 -0.1066368210 -0.2520011616
## 167 0 Drug 2 34 0.4703247602 -0.2520011616
## 168 0 Drug 3 34 -0.9212349396 -0.2520011616
## 169 0 Drug 4 34 -0.6166042017 -0.2520011616
## 170 0 Drug 5 34 0.1744754603 -0.2520011616
## 171 0 Placebo 1 35 0.4368954672 0.1234582831
## 172 1 Placebo 2 35 1.4196556000 0.1234582831
## 173 2 Placebo 3 35 -0.2309310605 0.1234582831
## 174 3 Placebo 4 35 -0.1342195510 0.1234582831
## 175 4 Placebo 5 35 -0.1186938695 0.1234582831
## 176 0 Placebo 1 36 0.2242043536 0.5043293035
## 177 1 Placebo 2 36 2.5427282034 0.5043293035
## 178 2 Placebo 3 36 -0.4169606976 0.5043293035
## 179 3 Placebo 4 36 -0.1543991222 0.5043293035
## 180 4 Placebo 5 36 1.5807086889 0.5043293035
## 181 0 Placebo 1 37 -0.7507053337 -0.4071628176
## 182 1 Placebo 2 37 0.1093127941 -0.4071628176
## 183 2 Placebo 3 37 -0.6494754158 -0.4071628176
## 184 3 Placebo 4 37 -1.1120948920 -0.4071628176
## 185 4 Placebo 5 37 0.1185234484 -0.4071628176
## 186 0 Drug 1 38 -0.0233569598 0.1192955203
## 187 0 Drug 2 38 1.8361380329 0.1192955203
## 188 0 Drug 3 38 0.3333926204 0.1192955203
## 189 0 Drug 4 38 -0.2566381603 0.1192955203
## 190 0 Drug 5 38 0.4584082805 0.1192955203
## 191 0 Placebo 1 39 0.2885494373 -0.2056909515
## 192 1 Placebo 2 39 0.1650565601 -0.2056909515
## 193 2 Placebo 3 39 -0.5075680932 -0.2056909515
## 194 3 Placebo 4 39 -0.8666801400 -0.2056909515
## 195 4 Placebo 5 39 1.3966507582 -0.2056909515
## 196 0 Placebo 1 40 2.4156231694 -0.2071113490
## 197 1 Placebo 2 40 0.2722210810 -0.2071113490
## 198 2 Placebo 3 40 0.3505846412 -0.2071113490
## 199 3 Placebo 4 40 0.4656235976 -0.2071113490
## 200 4 Placebo 5 40 -0.3595248420 -0.2071113490
## 201 0 Placebo 1 41 -0.3095886254 -0.0925769120
## 202 1 Placebo 2 41 -1.2595210200 -0.0925769120
## 203 2 Placebo 3 41 -0.4878811583 -0.0925769120
## 204 3 Placebo 4 41 0.6628704702 -0.0925769120
## 205 4 Placebo 5 41 -0.9906465799 -0.0925769120
## 206 0 Drug 1 42 -0.8137650364 -0.7939913341
## 207 0 Drug 2 42 -1.3858554703 -0.7939913341
## 208 0 Drug 3 42 0.3799995082 -0.7939913341
## 209 0 Drug 4 42 -0.3577023360 -0.7939913341
## 210 0 Drug 5 42 -0.9624613976 -0.7939913341
## 211 0 Drug 1 43 -0.8521681688 0.2750149628
## 212 0 Drug 2 43 0.7868097642 0.2750149628
## 213 0 Drug 3 43 -1.6036296454 0.2750149628
## 214 0 Drug 4 43 -0.8735274531 0.2750149628
## 215 0 Drug 5 43 1.0574609152 0.2750149628
## 216 0 Drug 1 44 -0.2756356956 -0.4744802636
## 217 0 Drug 2 44 1.7500039307 -0.4744802636
## 218 0 Drug 3 44 -1.0415590574 -0.4744802636
## 219 0 Drug 4 44 0.4827775070 -0.4744802636
## 220 0 Drug 5 44 -1.3182331846 -0.4744802636
## 221 0 Placebo 1 45 -0.4126426438 -0.3577680737
## 222 1 Placebo 2 45 -0.3489036709 -0.3577680737
## 223 2 Placebo 3 45 -0.8046509739 -0.3577680737
## 224 3 Placebo 4 45 -0.3835490162 -0.3577680737
## 225 4 Placebo 5 45 0.8712834751 -0.3577680737
## 226 0 Drug 1 46 0.8573683067 0.2411842358
## 227 0 Drug 2 46 0.6457716023 0.2411842358
## 228 0 Drug 3 46 0.5180249444 0.2411842358
## 229 0 Drug 4 46 -0.7046159274 0.2411842358
## 230 0 Drug 5 46 0.5547580130 0.2411842358
## 231 0 Placebo 1 47 -0.6376526443 0.0046682078
## 232 1 Placebo 2 47 -0.7277796604 0.0046682078
## 233 2 Placebo 3 47 -0.6129743078 0.0046682078
## 234 3 Placebo 4 47 0.3416220516 0.0046682078
## 235 4 Placebo 5 47 -1.6743932770 0.0046682078
## 236 0 Drug 1 48 0.6786376051 0.6606201758
## 237 0 Drug 2 48 0.8941451413 0.6606201758
## 238 0 Drug 3 48 -0.0688158903 0.6606201758
## 239 0 Drug 4 48 -1.1770574638 0.6606201758
## 240 0 Drug 5 48 -1.5022834937 0.6606201758
## 241 0 Placebo 1 49 0.1483687591 1.0351317821
## 242 1 Placebo 2 49 0.0342613957 1.0351317821
## 243 2 Placebo 3 49 1.1023444359 1.0351317821
## 244 3 Placebo 4 49 -0.8680605513 1.0351317821
## 245 4 Placebo 5 49 0.2664419974 1.0351317821
## 246 0 Placebo 1 50 -1.2639031782 -1.0684598899
## 247 1 Placebo 2 50 -0.8318101030 -1.0684598899
## 248 2 Placebo 3 50 -1.0251900354 -1.0684598899
## 249 3 Placebo 4 50 0.0787248955 -1.0684598899
## 250 4 Placebo 5 50 -0.5918419970 -1.0684598899
## 251 0 Drug 1 51 0.3279209002 0.1766964128
## 252 0 Drug 2 51 1.5485159920 0.1766964128
## 253 0 Drug 3 51 -0.6827768152 0.1766964128
## 254 0 Drug 4 51 1.7193312358 0.1766964128
## 255 0 Drug 5 51 -0.4255270545 0.1766964128
## 256 0 Drug 1 52 0.6633020750 0.0315776884
## 257 0 Drug 2 52 0.9323133634 0.0315776884
## 258 0 Drug 3 52 -1.0323960590 0.0315776884
## 259 0 Drug 4 52 0.0258773882 0.0315776884
## 260 0 Drug 5 52 -0.3262107789 0.0315776884
## 261 0 Drug 1 53 0.1819080356 -0.4569550076
## 262 0 Drug 2 53 -0.9446149733 -0.4569550076
## 263 0 Drug 3 53 -0.0182357203 -0.4569550076
## 264 0 Drug 4 53 0.4353599440 -0.4569550076
## 265 0 Drug 5 53 -0.5234793898 -0.4569550076
## 266 0 Drug 1 54 0.4828670049 -0.1486992395
## 267 0 Drug 2 54 0.1688662812 -0.1486992395
## 268 0 Drug 3 54 -0.4724286068 -0.1486992395
## 269 0 Drug 4 54 -1.7104132343 -0.1486992395
## 270 0 Drug 5 54 -1.6083858928 -0.1486992395
## 271 0 Placebo 1 55 -0.0743237340 -0.2452462978
## 272 1 Placebo 2 55 1.0891402256 -0.2452462978
## 273 2 Placebo 3 55 0.8482253908 -0.2452462978
## 274 3 Placebo 4 55 0.4297725717 -0.2452462978
## 275 4 Placebo 5 55 -0.7090075580 -0.2452462978
## 276 0 Placebo 1 56 1.5443266463 -0.0334339143
## 277 1 Placebo 2 56 1.1755702770 -0.0334339143
## 278 2 Placebo 3 56 0.8390498585 -0.0334339143
## 279 3 Placebo 4 56 -0.0470291325 -0.0334339143
## 280 4 Placebo 5 56 1.5455449798 -0.0334339143
## 281 0 Placebo 1 57 0.4127382463 0.0044162876
## 282 1 Placebo 2 57 1.9501940819 0.0044162876
## 283 2 Placebo 3 57 -0.3659601189 0.0044162876
## 284 3 Placebo 4 57 2.1691276982 0.0044162876
## 285 4 Placebo 5 57 0.7701591311 0.0044162876
## 286 0 Drug 1 58 -0.1891159481 0.3135231090
## 287 0 Drug 2 58 1.2682953058 0.3135231090
## 288 0 Drug 3 58 -0.6119205758 0.3135231090
## 289 0 Drug 4 58 -0.1670528347 0.3135231090
## 290 0 Drug 5 58 0.3112836083 0.3135231090
## 291 0 Drug 1 59 0.0599912685 -0.7144599298
## 292 0 Drug 2 59 -1.5149942862 -0.7144599298
## 293 0 Drug 3 59 2.0426219124 -0.7144599298
## 294 0 Drug 4 59 2.7498925057 -0.7144599298
## 295 0 Drug 5 59 1.1407416126 -0.7144599298
## 296 0 Drug 1 60 0.2656103995 0.0916712782
## 297 0 Drug 2 60 1.8922444670 0.0916712782
## 298 0 Drug 3 60 0.6053700789 0.0916712782
## 299 0 Drug 4 60 -0.5965865161 0.0916712782
## 300 0 Drug 5 60 0.3218955689 0.0916712782
## 301 0 Drug 1 1 0.5979954316 -0.3799843383
## 302 0 Drug 2 1 0.6131962940 -0.3799843383
## 303 0 Drug 3 1 -0.5424200450 -0.3799843383
## 304 0 Drug 4 1 -0.1931919210 -0.3799843383
## 305 0 Drug 5 1 1.4381686773 -0.3799843383
## 306 0 Drug 1 2 -0.4918686534 0.1753612642
## 307 0 Drug 2 2 1.5227437823 0.1753612642
## 308 0 Drug 3 2 0.4265228245 0.1753612642
## 309 0 Drug 4 2 0.2726209990 0.1753612642
## 310 0 Drug 5 2 2.3514418573 0.1753612642
## 311 0 Placebo 1 3 -0.3005714813 0.2506004956
## 312 1 Placebo 2 3 -1.1136032500 0.2506004956
## 313 2 Placebo 3 3 -1.1930584249 0.2506004956
## 314 3 Placebo 4 3 1.5841481304 0.2506004956
## 315 4 Placebo 5 3 0.7703790855 0.2506004956
## 316 0 Drug 1 4 1.3645370231 -0.0630647977
## 317 0 Drug 2 4 0.3111563437 -0.0630647977
## 318 0 Drug 3 4 -0.2348328648 -0.0630647977
## 319 0 Drug 4 4 -0.2210533049 -0.0630647977
## 320 0 Drug 5 4 0.2589160957 -0.0630647977
## 321 0 Drug 1 5 -0.3018720611 -0.0348031878
## 322 0 Drug 2 5 0.9687538789 -0.0348031878
## 323 0 Drug 3 5 -1.0938439054 -0.0348031878
## 324 0 Drug 4 5 -1.1733246087 -0.0348031878
## 325 0 Drug 5 5 0.2584661497 -0.0348031878
## 326 0 Placebo 1 6 1.5775915781 -0.1505143398
## 327 1 Placebo 2 6 1.0034641362 -0.1505143398
## 328 2 Placebo 3 6 -0.9003362183 -0.1505143398
## 329 3 Placebo 4 6 -1.7122619982 -0.1505143398
## 330 4 Placebo 5 6 1.7365004998 -0.1505143398
## 331 0 Placebo 1 7 -0.2049124232 0.0870682155
## 332 1 Placebo 2 7 -0.9670115969 0.0870682155
## 333 2 Placebo 3 7 -0.0761503649 0.0870682155
## 334 3 Placebo 4 7 -1.1339840512 0.0870682155
## 335 4 Placebo 5 7 -0.4982738924 0.0870682155
## 336 0 Placebo 1 8 0.0961875548 -0.0226480430
## 337 1 Placebo 2 8 -0.5679815052 -0.0226480430
## 338 2 Placebo 3 8 0.7821194341 -0.0226480430
## 339 3 Placebo 4 8 1.1208065505 -0.0226480430
## 340 4 Placebo 5 8 2.2649734201 -0.0226480430
## 341 0 Drug 1 9 -0.6375123227 0.0768848517
## 342 0 Drug 2 9 0.6445688599 0.0768848517
## 343 0 Drug 3 9 -1.7450148504 0.0768848517
## 344 0 Drug 4 9 1.6182677358 0.0768848517
## 345 0 Drug 5 9 0.1821685211 0.0768848517
## 346 0 Placebo 1 10 -1.5241135137 0.0093018100
## 347 1 Placebo 2 10 -0.1625256215 0.0093018100
## 348 2 Placebo 3 10 -0.1576767365 0.0093018100
## 349 3 Placebo 4 10 0.7377385358 0.0093018100
## 350 4 Placebo 5 10 1.0448251306 0.0093018100
## 351 0 Placebo 1 11 -1.0881965371 0.5388003677
## 352 1 Placebo 2 11 0.1709955553 0.5388003677
## 353 2 Placebo 3 11 -0.9178565596 0.5388003677
## 354 3 Placebo 4 11 -0.6421721136 0.5388003677
## 355 4 Placebo 5 11 0.1630283529 0.5388003677
## 356 0 Drug 1 12 -0.1112971501 -0.6866928998
## 357 0 Drug 2 12 -1.0364622014 -0.6866928998
## 358 0 Drug 3 12 0.1435472326 -0.6866928998
## 359 0 Drug 4 12 1.0739130192 -0.6866928998
## 360 0 Drug 5 12 0.6293841371 -0.6866928998
## 361 0 Drug 1 13 -0.5021202038 0.3943469930
## 362 0 Drug 2 13 -0.3624745630 0.3943469930
## 363 0 Drug 3 13 0.7293881380 0.3943469930
## 364 0 Drug 4 13 -0.2427414080 0.3943469930
## 365 0 Drug 5 13 0.2800741271 0.3943469930
## 366 0 Drug 1 14 0.8084480195 0.3350808339
## 367 0 Drug 2 14 -1.2117908904 0.3350808339
## 368 0 Drug 3 14 0.8041511365 0.3350808339
## 369 0 Drug 4 14 -0.8852789683 0.3350808339
## 370 0 Drug 5 14 -0.6111733712 0.3350808339
## 371 0 Placebo 1 15 -1.8344273834 0.0460618896
## 372 1 Placebo 2 15 -0.2610601987 0.0460618896
## 373 2 Placebo 3 15 0.6863224714 0.0460618896
## 374 3 Placebo 4 15 -0.1290702258 0.0460618896
## 375 4 Placebo 5 15 -0.5986546995 0.0460618896
## 376 0 Placebo 1 16 -1.9739346060 -0.3004007765
## 377 1 Placebo 2 16 0.5814824454 -0.3004007765
## 378 2 Placebo 3 16 -0.8911846349 -0.3004007765
## 379 3 Placebo 4 16 0.7096846654 -0.3004007765
## 380 4 Placebo 5 16 -0.2815214555 -0.3004007765
## 381 0 Placebo 1 17 -0.2562322025 0.1628493630
## 382 1 Placebo 2 17 -0.2018557267 0.1628493630
## 383 2 Placebo 3 17 0.0470242435 0.1628493630
## 384 3 Placebo 4 17 1.5643632204 0.1628493630
## 385 4 Placebo 5 17 2.1548519756 0.1628493630
## 386 0 Drug 1 18 0.8040797720 -0.0603501439
## 387 0 Drug 2 18 1.7541589849 -0.0603501439
## 388 0 Drug 3 18 1.9294617158 -0.0603501439
## 389 0 Drug 4 18 -0.2074994056 -0.0603501439
## 390 0 Drug 5 18 1.2659695231 -0.0603501439
## 391 0 Drug 1 19 0.1914364116 0.3053801701
## 392 0 Drug 2 19 -1.8963064929 0.3053801701
## 393 0 Drug 3 19 -0.7001492287 0.3053801701
## 394 0 Drug 4 19 -2.1033092423 0.3053801701
## 395 0 Drug 5 19 -0.5192690716 0.3053801701
## 396 0 Drug 1 20 1.1625148647 -0.1614793939
## 397 0 Drug 2 20 0.2366271595 -0.1614793939
## 398 0 Drug 3 20 -0.4398087450 -0.1614793939
## 399 0 Drug 4 20 0.1801804110 -0.1614793939
## 400 0 Drug 5 20 0.0607077573 -0.1614793939
## 401 0 Placebo 1 21 -1.2773235880 0.7243524605
## 402 1 Placebo 2 21 -1.0817316056 0.7243524605
## 403 2 Placebo 3 21 0.2330492804 0.7243524605
## 404 3 Placebo 4 21 -0.3411441889 0.7243524605
## 405 4 Placebo 5 21 -1.6602333031 0.7243524605
## 406 0 Placebo 1 22 0.2910816213 -0.3588862990
## 407 1 Placebo 2 22 0.7974849964 -0.3588862990
## 408 2 Placebo 3 22 0.2777394483 -0.3588862990
## 409 3 Placebo 4 22 -1.4035825877 -0.3588862990
## 410 4 Placebo 5 22 0.2357038510 -0.3588862990
## 411 0 Drug 1 23 -0.2380971460 -0.1256226964
## 412 0 Drug 2 23 -0.2879567813 -0.1256226964
## 413 0 Drug 3 23 -0.1412611816 -0.1256226964
## 414 0 Drug 4 23 -0.3217026966 -0.1256226964
## 415 0 Drug 5 23 0.1962526811 -0.1256226964
## 416 0 Drug 1 24 -0.2542930736 0.1309306728
## 417 0 Drug 2 24 0.4388009695 0.1309306728
## 418 0 Drug 3 24 -0.3596351678 0.1309306728
## 419 0 Drug 4 24 -1.3767650433 0.1309306728
## 420 0 Drug 5 24 -0.0101677048 0.1309306728
## 421 0 Placebo 1 25 0.3150125040 -0.5188003841
## 422 1 Placebo 2 25 -0.4183726219 -0.5188003841
## 423 2 Placebo 3 25 -0.5730131759 -0.5188003841
## 424 3 Placebo 4 25 -0.3438170462 -0.5188003841
## 425 4 Placebo 5 25 0.8956146719 -0.5188003841
## 426 0 Placebo 1 26 -2.7294063022 -0.3495057343
## 427 1 Placebo 2 26 -0.0633347087 -0.3495057343
## 428 2 Placebo 3 26 -1.8746015643 -0.3495057343
## 429 3 Placebo 4 26 0.9449001481 -0.3495057343
## 430 4 Placebo 5 26 0.8340632572 -0.3495057343
## 431 0 Placebo 1 27 0.1514401370 -0.5815894301
## 432 1 Placebo 2 27 -0.1629062334 -0.5815894301
## 433 2 Placebo 3 27 -0.0938387762 -0.5815894301
## 434 3 Placebo 4 27 -0.1617752617 -0.5815894301
## 435 4 Placebo 5 27 1.7695794487 -0.5815894301
## 436 0 Drug 1 28 0.0120020067 -0.2061567618
## 437 0 Drug 2 28 -0.4012348203 -0.2061567618
## 438 0 Drug 3 28 0.6496914962 -0.2061567618
## 439 0 Drug 4 28 -0.2812498814 -0.2061567618
## 440 0 Drug 5 28 3.2040503019 -0.2061567618
## 441 0 Drug 1 29 1.0799871403 -0.0325797514
## 442 0 Drug 2 29 0.8631047000 -0.0325797514
## 443 0 Drug 3 29 0.5810696093 -0.0325797514
## 444 0 Drug 4 29 -0.1413165517 -0.0325797514
## 445 0 Drug 5 29 -1.1340803137 -0.0325797514
## 446 0 Drug 1 30 1.3388258931 -0.2265049785
## 447 0 Drug 2 30 -1.6566045015 -0.2265049785
## 448 0 Drug 3 30 1.7384179048 -0.2265049785
## 449 0 Drug 4 30 -0.1673099722 -0.2265049785
## 450 0 Drug 5 30 -1.0990886356 -0.2265049785
## 451 0 Placebo 1 31 1.0183063479 0.3113733881
## 452 1 Placebo 2 31 -1.3082159241 0.3113733881
## 453 2 Placebo 3 31 1.9345454224 0.3113733881
## 454 3 Placebo 4 31 -0.3614407322 0.3113733881
## 455 4 Placebo 5 31 -0.4865900213 0.3113733881
## 456 0 Drug 1 32 -1.4455937854 -0.0968864750
## 457 0 Drug 2 32 0.3636048297 -0.0968864750
## 458 0 Drug 3 32 -0.6741751767 -0.0968864750
## 459 0 Drug 4 32 0.2616112434 -0.0968864750
## 460 0 Drug 5 32 -1.0469700899 -0.0968864750
## 461 0 Drug 1 33 1.8710964097 -0.1995531754
## 462 0 Drug 2 33 0.2693869256 -0.1995531754
## 463 0 Drug 3 33 0.0999469794 -0.1995531754
## 464 0 Drug 4 33 2.5550854975 -0.1995531754
## 465 0 Drug 5 33 -0.1493975917 -0.1995531754
## 466 0 Placebo 1 34 0.0137854276 -0.1704870715
## 467 1 Placebo 2 34 0.6154418673 -0.1704870715
## 468 2 Placebo 3 34 -0.5803065201 -0.1704870715
## 469 3 Placebo 4 34 0.8313347107 -0.1704870715
## 470 4 Placebo 5 34 1.9115550668 -0.1704870715
## 471 0 Drug 1 35 -0.7312095718 0.7161094414
## 472 0 Drug 2 35 -0.3276612299 0.7161094414
## 473 0 Drug 3 35 -0.6116729807 0.7161094414
## 474 0 Drug 4 35 1.5972794368 0.7161094414
## 475 0 Drug 5 35 -0.2763972558 0.7161094414
## 476 0 Drug 1 36 -0.4563971111 -0.5217097578
## 477 0 Drug 2 36 2.3876483008 -0.5217097578
## 478 0 Drug 3 36 0.1489134200 -0.5217097578
## 479 0 Drug 4 36 0.7942774647 -0.5217097578
## 480 0 Drug 5 36 0.6225454454 -0.5217097578
## 481 0 Placebo 1 37 1.0477268784 -0.2725569989
## 482 1 Placebo 2 37 1.2386534692 -0.2725569989
## 483 2 Placebo 3 37 0.8731040447 -0.2725569989
## 484 3 Placebo 4 37 0.6629541578 -0.2725569989
## 485 4 Placebo 5 37 -1.2192293610 -0.2725569989
## 486 0 Drug 1 38 1.2039396083 0.0873010275
## 487 0 Drug 2 38 -1.7775892517 0.0873010275
## 488 0 Drug 3 38 -0.9485415380 0.0873010275
## 489 0 Drug 4 38 0.0427587637 0.0873010275
## 490 0 Drug 5 38 1.2563575030 0.0873010275
## 491 0 Drug 1 39 -0.4799849170 0.1244798960
## 492 0 Drug 2 39 1.2641626310 0.1244798960
## 493 0 Drug 3 39 -0.7450505951 0.1244798960
## 494 0 Drug 4 39 0.0136184571 0.1244798960
## 495 0 Drug 5 39 -0.3268406729 0.1244798960
## 496 0 Placebo 1 40 -1.8257000298 0.1610392925
## 497 1 Placebo 2 40 -0.6191637012 0.1610392925
## 498 2 Placebo 3 40 0.2140069141 0.1610392925
## 499 3 Placebo 4 40 -0.1559975802 0.1610392925
## 500 4 Placebo 5 40 1.4387736131 0.1610392925
## 501 0 Drug 1 41 0.2295078367 0.0728889840
## 502 0 Drug 2 41 -0.1400755838 0.0728889840
## 503 0 Drug 3 41 1.0342977517 0.0728889840
## 504 0 Drug 4 41 0.3569950635 0.0728889840
## 505 0 Drug 5 41 -0.2292419454 0.0728889840
## 506 0 Placebo 1 42 0.4443476480 0.4289182542
## 507 1 Placebo 2 42 2.3644762005 0.4289182542
## 508 2 Placebo 3 42 -0.6151711302 0.4289182542
## 509 3 Placebo 4 42 -0.4063856567 0.4289182542
## 510 4 Placebo 5 42 0.2714188150 0.4289182542
## 511 0 Drug 1 43 -0.5617704313 0.2088824406
## 512 0 Drug 2 43 -0.4907093676 0.2088824406
## 513 0 Drug 3 43 0.5113497319 0.2088824406
## 514 0 Drug 4 43 -1.0160794244 0.2088824406
## 515 0 Drug 5 43 0.2045811493 0.2088824406
## 516 0 Placebo 1 44 -1.2454463561 -0.3188911973
## 517 1 Placebo 2 44 -1.5200509428 -0.3188911973
## 518 2 Placebo 3 44 -0.2460846982 -0.3188911973
## 519 3 Placebo 4 44 0.1149871532 -0.3188911973
## 520 4 Placebo 5 44 1.2934611020 -0.3188911973
## 521 0 Drug 1 45 -0.1573885267 0.3994620521
## 522 0 Drug 2 45 -0.1856449127 0.3994620521
## 523 0 Drug 3 45 -0.3727052580 0.3994620521
## 524 0 Drug 4 45 -0.7812345448 0.3994620521
## 525 0 Drug 5 45 -0.4813690571 0.3994620521
## 526 0 Placebo 1 46 0.3649440364 -0.0738738573
## 527 1 Placebo 2 46 -1.0013048902 -0.0738738573
## 528 2 Placebo 3 46 0.0371343033 -0.0738738573
## 529 3 Placebo 4 46 -0.7376986441 -0.0738738573
## 530 4 Placebo 5 46 -1.2269959591 -0.0738738573
## 531 0 Placebo 1 47 0.5417771426 -0.0459384504
## 532 1 Placebo 2 47 0.2696510248 -0.0459384504
## 533 2 Placebo 3 47 0.7334264899 -0.0459384504
## 534 3 Placebo 4 47 0.7098845373 -0.0459384504
## 535 4 Placebo 5 47 -0.3455950599 -0.0459384504
## 536 0 Placebo 1 48 -1.2239408718 0.1733455410
## 537 1 Placebo 2 48 -0.8885666950 0.1733455410
## 538 2 Placebo 3 48 1.7426619471 0.1733455410
## 539 3 Placebo 4 48 0.1244401136 0.1733455410
## 540 4 Placebo 5 48 0.8989858067 0.1733455410
## 541 0 Placebo 1 49 0.8764796476 0.1361683630
## 542 1 Placebo 2 49 -0.1914379247 0.1361683630
## 543 2 Placebo 3 49 -0.2930984789 0.1361683630
## 544 3 Placebo 4 49 -0.2381699617 0.1361683630
## 545 4 Placebo 5 49 -0.8379857947 0.1361683630
## 546 0 Drug 1 50 -1.3632571292 -0.1809533430
## 547 0 Drug 2 50 0.0010527653 -0.1809533430
## 548 0 Drug 3 50 1.1868524616 -0.1809533430
## 549 0 Drug 4 50 -0.6688858544 -0.1809533430
## 550 0 Drug 5 50 -0.6515315818 -0.1809533430
## 551 0 Drug 1 51 -0.3484163176 0.2132662725
## 552 0 Drug 2 51 -1.3030520858 0.2132662725
## 553 0 Drug 3 51 -0.0744406183 0.2132662725
## 554 0 Drug 4 51 -0.4525789747 0.2132662725
## 555 0 Drug 5 51 0.8925683286 0.2132662725
## 556 0 Drug 1 52 1.9607715434 -0.8545692831
## 557 0 Drug 2 52 1.0861098023 -0.8545692831
## 558 0 Drug 3 52 -0.8998301596 -0.8545692831
## 559 0 Drug 4 52 -2.2670466616 -0.8545692831
## 560 0 Drug 5 52 -0.5373902924 -0.8545692831
## 561 0 Placebo 1 53 0.1526958406 -0.6581641424
## 562 1 Placebo 2 53 -0.0769106642 -0.6581641424
## 563 2 Placebo 3 53 0.2673200807 -0.6581641424
## 564 3 Placebo 4 53 0.2523775345 -0.6581641424
## 565 4 Placebo 5 53 -1.2530867894 -0.6581641424
## 566 0 Placebo 1 54 -1.4591131752 0.0416565290
## 567 1 Placebo 2 54 0.1079811721 0.0416565290
## 568 2 Placebo 3 54 -0.1464367603 0.0416565290
## 569 3 Placebo 4 54 -0.6668732290 0.0416565290
## 570 4 Placebo 5 54 0.0358311689 0.0416565290
## 571 0 Placebo 1 55 0.8090561067 0.5490726237
## 572 1 Placebo 2 55 1.4837193886 0.5490726237
## 573 2 Placebo 3 55 0.6932552189 0.5490726237
## 574 3 Placebo 4 55 0.0323965946 0.5490726237
## 575 4 Placebo 5 55 0.7270467570 0.5490726237
## 576 0 Drug 1 56 -0.9145335267 -0.5217662994
## 577 0 Drug 2 56 -1.1693562062 -0.5217662994
## 578 0 Drug 3 56 0.5633283238 -0.5217662994
## 579 0 Drug 4 56 0.6387296945 -0.5217662994
## 580 0 Drug 5 56 -0.2984620134 -0.5217662994
## 581 0 Drug 1 57 0.1841129378 0.4276516832
## 582 0 Drug 2 57 1.2786912520 0.4276516832
## 583 0 Drug 3 57 1.0719214356 0.4276516832
## 584 0 Drug 4 57 0.4042765405 0.4276516832
## 585 0 Drug 5 57 0.7376753372 0.4276516832
## 586 0 Drug 1 58 0.0798999382 -0.6868796670
## 587 0 Drug 2 58 -0.7263401042 -0.6868796670
## 588 0 Drug 3 58 0.0825751094 -0.6868796670
## 589 0 Drug 4 58 1.5096052373 -0.6868796670
## 590 0 Drug 5 58 -0.0142431947 -0.6868796670
## 591 0 Placebo 1 59 1.3746998288 0.4202201203
## 592 1 Placebo 2 59 -0.4327649028 0.4202201203
## 593 2 Placebo 3 59 1.7017730627 0.4202201203
## 594 3 Placebo 4 59 -0.2110209587 0.4202201203
## 595 4 Placebo 5 59 0.5758911400 0.4202201203
## 596 0 Placebo 1 60 -0.9874554607 0.0310440576
## 597 1 Placebo 2 60 0.2176824554 0.0310440576
## 598 2 Placebo 3 60 0.3407633654 0.0310440576
## 599 3 Placebo 4 60 0.9555614791 0.0310440576
## 600 4 Placebo 5 60 0.2368069875 0.0310440576
## 601 0 Placebo 1 1 -0.6359089065 -0.0124182216
## 602 1 Placebo 2 1 -0.5313180930 -0.0124182216
## 603 2 Placebo 3 1 -1.5135831893 -0.0124182216
## 604 3 Placebo 4 1 1.6379589740 -0.0124182216
## 605 4 Placebo 5 1 0.3775471381 -0.0124182216
## 606 0 Placebo 1 2 -1.5568939231 0.5488962828
## 607 1 Placebo 2 2 0.7741307079 0.5488962828
## 608 2 Placebo 3 2 0.1758637604 0.5488962828
## 609 3 Placebo 4 2 -1.0158326123 0.5488962828
## 610 4 Placebo 5 2 0.1539632296 0.5488962828
## 611 0 Placebo 1 3 0.4595751042 -0.2363313217
## 612 1 Placebo 2 3 -0.9207368067 -0.2363313217
## 613 2 Placebo 3 3 1.6436047028 -0.2363313217
## 614 3 Placebo 4 3 -1.4113772127 -0.2363313217
## 615 4 Placebo 5 3 0.2658758538 -0.2363313217
## 616 0 Drug 1 4 0.2462003521 -0.7294582828
## 617 0 Drug 2 4 -1.6442659750 -0.7294582828
## 618 0 Drug 3 4 -0.6488361557 -0.7294582828
## 619 0 Drug 4 4 -1.4809229806 -0.7294582828
## 620 0 Drug 5 4 0.3130672577 -0.7294582828
## 621 0 Drug 1 5 1.1300242495 0.7889883584
## 622 0 Drug 2 5 -0.5391542646 0.7889883584
## 623 0 Drug 3 5 1.5234560817 0.7889883584
## 624 0 Drug 4 5 -1.0258686350 0.7889883584
## 625 0 Drug 5 5 1.0773138004 0.7889883584
## 626 0 Drug 1 6 0.0875564445 -0.3488158086
## 627 0 Drug 2 6 -0.8189591860 -0.3488158086
## 628 0 Drug 3 6 -0.9360457708 -0.3488158086
## 629 0 Drug 4 6 -1.5131165138 -0.3488158086
## 630 0 Drug 5 6 1.4448197298 -0.3488158086
## 631 0 Placebo 1 7 -0.7580678249 -0.2030661992
## 632 1 Placebo 2 7 -3.0001362741 -0.2030661992
## 633 2 Placebo 3 7 1.9682900914 -0.2030661992
## 634 3 Placebo 4 7 0.0917310078 -0.2030661992
## 635 4 Placebo 5 7 -0.1237924035 -0.2030661992
## 636 0 Drug 1 8 2.3127426448 -0.0515035951
## 637 0 Drug 2 8 0.1896635352 -0.0515035951
## 638 0 Drug 3 8 0.2320630070 -0.0515035951
## 639 0 Drug 4 8 -0.0006048391 -0.0515035951
## 640 0 Drug 5 8 0.5173840974 -0.0515035951
## 641 0 Placebo 1 9 0.1118017654 0.0007636665
## 642 1 Placebo 2 9 0.4720948875 0.0007636665
## 643 2 Placebo 3 9 -0.5527707783 0.0007636665
## 644 3 Placebo 4 9 0.1029312734 0.0007636665
## 645 4 Placebo 5 9 0.0142164993 0.0007636665
## 646 0 Drug 1 10 0.8600361908 -0.3974371652
## 647 0 Drug 2 10 -0.5637286881 -0.3974371652
## 648 0 Drug 3 10 -0.3342696806 -0.3974371652
## 649 0 Drug 4 10 -1.4757632729 -0.3974371652
## 650 0 Drug 5 10 0.5907745854 -0.3974371652
## 651 0 Drug 1 11 0.7334001369 0.0089879637
## 652 0 Drug 2 11 1.1140922226 0.0089879637
## 653 0 Drug 3 11 -1.4731599960 0.0089879637
## 654 0 Drug 4 11 0.8841389698 0.0089879637
## 655 0 Drug 5 11 -0.8635801409 0.0089879637
## 656 0 Placebo 1 12 -0.2135298457 -0.1111166440
## 657 1 Placebo 2 12 -1.1898211238 -0.1111166440
## 658 2 Placebo 3 12 -1.2998029664 -0.1111166440
## 659 3 Placebo 4 12 -0.1758075568 -0.1111166440
## 660 4 Placebo 5 12 -0.9541343173 -0.1111166440
## 661 0 Placebo 1 13 0.1516917409 1.1036178064
## 662 1 Placebo 2 13 -0.7448143867 1.1036178064
## 663 2 Placebo 3 13 0.4437337420 1.1036178064
## 664 3 Placebo 4 13 -1.5970595336 1.1036178064
## 665 4 Placebo 5 13 -0.6706109329 1.1036178064
## 666 0 Placebo 1 14 -1.3455838274 -0.3088163046
## 667 1 Placebo 2 14 0.3831787099 -0.3088163046
## 668 2 Placebo 3 14 0.2608842971 -0.3088163046
## 669 3 Placebo 4 14 0.8952761171 -0.3088163046
## 670 4 Placebo 5 14 0.2074497481 -0.3088163046
## 671 0 Placebo 1 15 0.4989982675 -0.4978073826
## 672 1 Placebo 2 15 -0.2902812563 -0.4978073826
## 673 2 Placebo 3 15 -1.5329825890 -0.4978073826
## 674 3 Placebo 4 15 0.8365467337 -0.4978073826
## 675 4 Placebo 5 15 -0.4252594041 -0.4978073826
## 676 0 Drug 1 16 -0.0190044833 -0.0005776546
## 677 0 Drug 2 16 0.5925255212 -0.0005776546
## 678 0 Drug 3 16 0.1614059373 -0.0005776546
## 679 0 Drug 4 16 -1.3552305820 -0.0005776546
## 680 0 Drug 5 16 -1.9364474425 -0.0005776546
## 681 0 Placebo 1 17 -0.3884535491 -0.5407720858
## 682 1 Placebo 2 17 -0.1803858846 -0.5407720858
## 683 2 Placebo 3 17 0.0499716726 -0.5407720858
## 684 3 Placebo 4 17 0.3899788707 -0.5407720858
## 685 4 Placebo 5 17 0.2753437101 -0.5407720858
## 686 0 Placebo 1 18 0.9645467284 -0.3838561011
## 687 1 Placebo 2 18 -0.8977989378 -0.3838561011
## 688 2 Placebo 3 18 -0.6524826862 -0.3838561011
## 689 3 Placebo 4 18 0.5348488951 -0.3838561011
## 690 4 Placebo 5 18 1.5826473254 -0.3838561011
## 691 0 Drug 1 19 -0.0024324752 0.2748297395
## 692 0 Drug 2 19 -0.2493728896 0.2748297395
## 693 0 Drug 3 19 0.0280758191 0.2748297395
## 694 0 Drug 4 19 0.1812129915 0.2748297395
## 695 0 Drug 5 19 0.5778100766 0.2748297395
## 696 0 Placebo 1 20 -0.1110177265 0.3566071496
## 697 1 Placebo 2 20 -1.5901434070 0.3566071496
## 698 2 Placebo 3 20 0.4759847038 0.3566071496
## 699 3 Placebo 4 20 -1.6774805283 0.3566071496
## 700 4 Placebo 5 20 0.9778948246 0.3566071496
## 701 0 Placebo 1 21 -0.1027778221 0.2784570433
## 702 1 Placebo 2 21 -1.2544253129 0.2784570433
## 703 2 Placebo 3 21 -0.1075468523 0.2784570433
## 704 3 Placebo 4 21 -0.3672137003 0.2784570433
## 705 4 Placebo 5 21 0.2050821402 0.2784570433
## 706 0 Placebo 1 22 -0.5591171086 0.1968523922
## 707 1 Placebo 2 22 -1.0081876448 0.1968523922
## 708 2 Placebo 3 22 -0.1129249858 0.1968523922
## 709 3 Placebo 4 22 -0.0087621167 0.1968523922
## 710 4 Placebo 5 22 -3.3161812682 0.1968523922
## 711 0 Drug 1 23 0.4374079453 -0.4897879902
## 712 0 Drug 2 23 0.1532173302 -0.4897879902
## 713 0 Drug 3 23 -0.1600571251 -0.4897879902
## 714 0 Drug 4 23 -0.3318667784 -0.4897879902
## 715 0 Drug 5 23 -0.5471576792 -0.4897879902
## 716 0 Drug 1 24 1.3565780693 0.0625239738
## 717 0 Drug 2 24 0.3802917564 0.0625239738
## 718 0 Drug 3 24 0.2658880087 0.0625239738
## 719 0 Drug 4 24 -1.4078013130 0.0625239738
## 720 0 Drug 5 24 -0.8932543302 0.0625239738
## 721 0 Placebo 1 25 0.9577543081 0.2062427620
## 722 1 Placebo 2 25 -0.3209807376 0.2062427620
## 723 2 Placebo 3 25 1.1055359370 0.2062427620
## 724 3 Placebo 4 25 -1.3695214471 0.2062427620
## 725 4 Placebo 5 25 0.6623896106 0.2062427620
## 726 0 Placebo 1 26 0.8794745880 -0.3938877205
## 727 1 Placebo 2 26 -0.5497724595 -0.3938877205
## 728 2 Placebo 3 26 0.4659202943 -0.3938877205
## 729 3 Placebo 4 26 0.1708494434 -0.3938877205
## 730 4 Placebo 5 26 1.7142242944 -0.3938877205
## 731 0 Placebo 1 27 0.3686065787 0.2916461306
## 732 1 Placebo 2 27 -0.0239671469 0.2916461306
## 733 2 Placebo 3 27 -0.3684974497 0.2916461306
## 734 3 Placebo 4 27 1.3061164307 0.2916461306
## 735 4 Placebo 5 27 -0.7453923662 0.2916461306
## 736 0 Drug 1 28 0.7264704596 -0.0265658650
## 737 0 Drug 2 28 -0.7182555846 -0.0265658650
## 738 0 Drug 3 28 0.2593077636 -0.0265658650
## 739 0 Drug 4 28 1.0577307269 -0.0265658650
## 740 0 Drug 5 28 2.7769136571 -0.0265658650
## 741 0 Placebo 1 29 2.4380843461 -0.1640778212
## 742 1 Placebo 2 29 0.5243371490 -0.1640778212
## 743 2 Placebo 3 29 0.3722061308 -0.1640778212
## 744 3 Placebo 4 29 0.9297011301 -0.1640778212
## 745 4 Placebo 5 29 0.8658956755 -0.1640778212
## 746 0 Drug 1 30 1.1758970456 -1.1308971068
## 747 0 Drug 2 30 0.0766299761 -1.1308971068
## 748 0 Drug 3 30 -0.7064793216 -1.1308971068
## 749 0 Drug 4 30 -0.2946217178 -1.1308971068
## 750 0 Drug 5 30 1.0872836054 -1.1308971068
## 751 0 Drug 1 31 -0.5675158314 -0.3364280213
## 752 0 Drug 2 31 -0.0416737870 -0.3364280213
## 753 0 Drug 3 31 0.0788491847 -0.3364280213
## 754 0 Drug 4 31 0.4481294794 -0.3364280213
## 755 0 Drug 5 31 -1.5729126733 -0.3364280213
## 756 0 Placebo 1 32 -1.5067645087 -0.0448320258
## 757 1 Placebo 2 32 -0.5783646101 -0.0448320258
## 758 2 Placebo 3 32 -0.9509738600 -0.0448320258
## 759 3 Placebo 4 32 -1.2448710586 -0.0448320258
## 760 4 Placebo 5 32 0.5338231405 -0.0448320258
## 761 0 Drug 1 33 1.9164552602 0.3263464761
## 762 0 Drug 2 33 -0.1662749108 0.3263464761
## 763 0 Drug 3 33 0.3903477995 0.3263464761
## 764 0 Drug 4 33 -1.5869979387 0.3263464761
## 765 0 Drug 5 33 -0.2850762478 0.3263464761
## 766 0 Placebo 1 34 -0.2831022842 0.2344688350
## 767 1 Placebo 2 34 -0.7578808674 0.2344688350
## 768 2 Placebo 3 34 0.8180820579 0.2344688350
## 769 3 Placebo 4 34 -0.2155902272 0.2344688350
## 770 4 Placebo 5 34 -0.2159616053 0.2344688350
## 771 0 Drug 1 35 -2.3094897656 0.1011030035
## 772 0 Drug 2 35 -0.5513576667 0.1011030035
## 773 0 Drug 3 35 0.5027950654 0.1011030035
## 774 0 Drug 4 35 1.0980258183 0.1011030035
## 775 0 Drug 5 35 0.0410966674 0.1011030035
## 776 0 Placebo 1 36 -0.1062126436 0.9748385857
## 777 1 Placebo 2 36 1.0511700043 0.9748385857
## 778 2 Placebo 3 36 1.0590488979 0.9748385857
## 779 3 Placebo 4 36 -0.0350746253 0.9748385857
## 780 4 Placebo 5 36 -0.8772752483 0.9748385857
## 781 0 Drug 1 37 0.5384322747 0.1448335167
## 782 0 Drug 2 37 0.4300934892 0.1448335167
## 783 0 Drug 3 37 0.0503095411 0.1448335167
## 784 0 Drug 4 37 1.1835940033 0.1448335167
## 785 0 Drug 5 37 1.4460455944 0.1448335167
## 786 0 Placebo 1 38 0.6677186986 0.0784079149
## 787 1 Placebo 2 38 -0.1264038180 0.0784079149
## 788 2 Placebo 3 38 -0.6849622804 0.0784079149
## 789 3 Placebo 4 38 -0.5520900008 0.0784079149
## 790 4 Placebo 5 38 1.3087491556 0.0784079149
## 791 0 Drug 1 39 -1.1652242001 0.4490426898
## 792 0 Drug 2 39 0.9268543636 0.4490426898
## 793 0 Drug 3 39 -0.8137418748 0.4490426898
## 794 0 Drug 4 39 -0.4853086943 0.4490426898
## 795 0 Drug 5 39 0.0708777642 0.4490426898
## 796 0 Drug 1 40 0.8600447090 0.2297567809
## 797 0 Drug 2 40 -0.9453414165 0.2297567809
## 798 0 Drug 3 40 0.9208450522 0.2297567809
## 799 0 Drug 4 40 -1.4441733742 0.2297567809
## 800 0 Drug 5 40 0.0468059876 0.2297567809
## 801 0 Drug 1 41 -0.3340585349 -0.5628062336
## 802 0 Drug 2 41 -0.6512872935 -0.5628062336
## 803 0 Drug 3 41 1.6414113782 -0.5628062336
## 804 0 Drug 4 41 0.8147982045 -0.5628062336
## 805 0 Drug 5 41 -0.0916430749 -0.5628062336
## 806 0 Placebo 1 42 2.1979591001 0.6477489898
## 807 1 Placebo 2 42 1.4651937595 0.6477489898
## 808 2 Placebo 3 42 -1.1316459685 0.6477489898
## 809 3 Placebo 4 42 0.9169991836 0.6477489898
## 810 4 Placebo 5 42 -1.2372959802 0.6477489898
## 811 0 Placebo 1 43 -0.7082337184 -0.8600501395
## 812 1 Placebo 2 43 0.0979128219 -0.8600501395
## 813 2 Placebo 3 43 0.3113074304 -0.8600501395
## 814 3 Placebo 4 43 1.7711880963 -0.8600501395
## 815 4 Placebo 5 43 0.1316615864 -0.8600501395
## 816 0 Placebo 1 44 -0.3541556758 0.6363263155
## 817 1 Placebo 2 44 0.2893719030 0.6363263155
## 818 2 Placebo 3 44 0.8685213453 0.6363263155
## 819 3 Placebo 4 44 0.6170822088 0.6363263155
## 820 4 Placebo 5 44 -0.7577206773 0.6363263155
## 821 0 Drug 1 45 0.6011180297 0.2847993578
## 822 0 Drug 2 45 -0.5486550569 0.2847993578
## 823 0 Drug 3 45 1.1100958577 0.2847993578
## 824 0 Drug 4 45 0.5647164628 0.2847993578
## 825 0 Drug 5 45 1.0077047217 0.2847993578
## 826 0 Placebo 1 46 -1.0564572626 -0.2264678216
## 827 1 Placebo 2 46 -0.4791408977 -0.2264678216
## 828 2 Placebo 3 46 -0.5451391578 -0.2264678216
## 829 3 Placebo 4 46 -0.7019395466 -0.2264678216
## 830 4 Placebo 5 46 -0.8417963735 -0.2264678216
## 831 0 Placebo 1 47 -1.4463264768 0.6954626702
## 832 1 Placebo 2 47 0.4742703891 0.6954626702
## 833 2 Placebo 3 47 0.1399952606 0.6954626702
## 834 3 Placebo 4 47 -0.1599254985 0.6954626702
## 835 4 Placebo 5 47 -0.1426333251 0.6954626702
## 836 0 Drug 1 48 1.1650602086 -0.3773560800
## 837 0 Drug 2 48 -0.0127573774 -0.3773560800
## 838 0 Drug 3 48 -0.4517848666 -0.3773560800
## 839 0 Drug 4 48 1.4505000872 -0.3773560800
## 840 0 Drug 5 48 1.9404454486 -0.3773560800
## 841 0 Placebo 1 49 -0.4697905632 0.5602374225
## 842 1 Placebo 2 49 0.7110672509 0.5602374225
## 843 2 Placebo 3 49 1.5351824246 0.5602374225
## 844 3 Placebo 4 49 1.2611917125 0.5602374225
## 845 4 Placebo 5 49 0.3682668588 0.5602374225
## 846 0 Placebo 1 50 0.1688507952 0.2103791336
## 847 1 Placebo 2 50 -1.8479479634 0.2103791336
## 848 2 Placebo 3 50 1.5557372111 0.2103791336
## 849 3 Placebo 4 50 0.4868668238 0.2103791336
## 850 4 Placebo 5 50 -0.1455836240 0.2103791336
## 851 0 Placebo 1 51 -2.0358954438 -0.2016022496
## 852 1 Placebo 2 51 0.9416417156 -0.2016022496
## 853 2 Placebo 3 51 0.1698161743 -0.2016022496
## 854 3 Placebo 4 51 -0.2741791309 -0.2016022496
## 855 4 Placebo 5 51 0.1433695788 -0.2016022496
## 856 0 Placebo 1 52 -0.1051506585 -0.1009182101
## 857 1 Placebo 2 52 -0.6086222823 -0.1009182101
## 858 2 Placebo 3 52 0.6778468091 -0.1009182101
## 859 3 Placebo 4 52 1.2448409601 -0.1009182101
## 860 4 Placebo 5 52 -1.1162009486 -0.1009182101
## 861 0 Drug 1 53 0.5782486945 0.1927255524
## 862 0 Drug 2 53 -0.2443003673 0.1927255524
## 863 0 Drug 3 53 -0.7810739493 0.1927255524
## 864 0 Drug 4 53 0.1928622031 0.1927255524
## 865 0 Drug 5 53 1.1394125238 0.1927255524
## 866 0 Placebo 1 54 0.2391604317 0.5209444467
## 867 1 Placebo 2 54 -0.9763144786 0.5209444467
## 868 2 Placebo 3 54 0.5237580554 0.5209444467
## 869 3 Placebo 4 54 0.6982233105 0.5209444467
## 870 4 Placebo 5 54 1.3214265567 0.5209444467
## 871 0 Drug 1 55 0.4086104192 0.5182508280
## 872 0 Drug 2 55 2.2841620056 0.5182508280
## 873 0 Drug 3 55 -0.6965677290 0.5182508280
## 874 0 Drug 4 55 0.0051403976 0.5182508280
## 875 0 Drug 5 55 1.3823151220 0.5182508280
## 876 0 Drug 1 56 -0.4829603541 -0.5731007304
## 877 0 Drug 2 56 -0.2212986436 -0.5731007304
## 878 0 Drug 3 56 0.7410613401 -0.5731007304
## 879 0 Drug 4 56 0.0960147570 -0.5731007304
## 880 0 Drug 5 56 -1.3729141885 -0.5731007304
## 881 0 Placebo 1 57 -0.2818646335 0.0958979056
## 882 1 Placebo 2 57 0.5323368768 0.0958979056
## 883 2 Placebo 3 57 0.2683779049 0.0958979056
## 884 3 Placebo 4 57 -0.8035503821 0.0958979056
## 885 4 Placebo 5 57 0.8096957419 0.0958979056
## 886 0 Placebo 1 58 -0.0238548084 0.1470837565
## 887 1 Placebo 2 58 0.1624553847 0.1470837565
## 888 2 Placebo 3 58 -0.2971493738 0.1470837565
## 889 3 Placebo 4 58 -0.2343656741 0.1470837565
## 890 4 Placebo 5 58 -0.6590053622 0.1470837565
## 891 0 Drug 1 59 -0.4912740253 -0.0593148097
## 892 0 Drug 2 59 1.5923776690 -0.0593148097
## 893 0 Drug 3 59 0.5760883773 -0.0593148097
## 894 0 Drug 4 59 -0.5069596234 -0.0593148097
## 895 0 Drug 5 59 0.2563260441 -0.0593148097
## 896 0 Placebo 1 60 -0.6903758889 0.1138152130
## 897 1 Placebo 2 60 -1.0299154379 0.1138152130
## 898 2 Placebo 3 60 0.1553920135 0.1138152130
## 899 3 Placebo 4 60 -0.3882406195 0.1138152130
## 900 4 Placebo 5 60 -0.6170005538 0.1138152130
## time_clust fixed_outcome random_effects resp_var
## 1 -0.675689527 4.75 -0.0749605335 5.3491331
## 2 -0.675689527 5.25 -0.7506500602 4.5231653
## 3 -0.675689527 5.75 -1.4263395869 5.6843465
## 4 -0.675689527 6.25 -2.1020291136 3.2122060
## 5 -0.675689527 6.75 -2.7777186403 6.3013226
## 6 -0.428122035 4.00 -0.3988322796 3.9417706
## 7 -0.428122035 4.50 -0.8269543146 4.3982231
## 8 -0.428122035 5.00 -1.2550763496 2.7840412
## 9 -0.428122035 5.50 -1.6831983846 2.6096733
## 10 -0.428122035 6.00 -2.1113204196 4.1608253
## 11 -0.764881609 4.75 -0.5635761480 4.5443557
## 12 -0.764881609 5.25 -1.3284577574 2.7273315
## 13 -0.764881609 5.75 -2.0933393667 3.5998730
## 14 -0.764881609 6.25 -2.8582209761 4.1227714
## 15 -0.764881609 6.75 -3.6231025854 3.0775825
## 16 -1.031126780 4.00 0.4010985233 3.7393899
## 17 -1.031126780 4.50 -0.6300282570 4.7548270
## 18 -1.031126780 5.00 -1.6611550373 2.2383383
## 19 -1.031126780 5.50 -2.6922818175 2.1622511
## 20 -1.031126780 6.00 -3.7234085978 3.4502866
## 21 -0.870154677 4.00 0.1941890939 4.6647346
## 22 -0.870154677 4.50 -0.6759655829 3.6185806
## 23 -0.870154677 5.00 -1.5461202597 3.8607324
## 24 -0.870154677 5.50 -2.4162749365 3.0792519
## 25 -0.870154677 6.00 -3.2864296134 3.4185451
## 26 -0.206935831 4.75 -0.4969617196 4.9272012
## 27 -0.206935831 5.25 -0.7038975507 4.7479833
## 28 -0.206935831 5.75 -0.9108333818 5.5934617
## 29 -0.206935831 6.25 -1.1177692129 4.4414988
## 30 -0.206935831 6.75 -1.3247050440 4.7295601
## 31 0.592603524 4.75 0.0505374684 5.1797580
## 32 0.592603524 5.25 0.6431409926 6.0406827
## 33 0.592603524 5.75 1.2357445168 6.4526878
## 34 0.592603524 6.25 1.8283480410 7.4379558
## 35 0.592603524 6.75 2.4209515652 8.7660650
## 36 0.077665378 4.00 0.4462609800 3.1745427
## 37 0.077665378 4.50 0.5239263583 4.5646319
## 38 0.077665378 5.00 0.6015917366 4.2858488
## 39 0.077665378 5.50 0.6792571149 7.2638610
## 40 0.077665378 6.00 0.7569224932 7.9523883
## 41 -0.276921528 4.75 -0.2476761974 3.0165082
## 42 -0.276921528 5.25 -0.5245977254 5.5705062
## 43 -0.276921528 5.75 -0.8015192533 7.5229433
## 44 -0.276921528 6.25 -1.0784407812 4.7769906
## 45 -0.276921528 6.75 -1.3553623091 4.9819278
## 46 -0.314154896 4.00 -0.1913466236 3.5931069
## 47 -0.314154896 4.50 -0.5055015199 3.4384529
## 48 -0.314154896 5.00 -0.8196564163 4.9994629
## 49 -0.314154896 5.50 -1.1338113126 3.7175982
## 50 -0.314154896 6.00 -1.4479662089 5.6300706
## 51 0.003876981 4.75 0.4963496675 4.6419155
## 52 0.003876981 5.25 0.5002266488 6.2787613
## 53 0.003876981 5.75 0.5041036302 6.6306413
## 54 0.003876981 6.25 0.5079806115 6.6241609
## 55 0.003876981 6.75 0.5118575928 5.7096335
## 56 0.590545833 4.75 0.1499713786 4.4331553
## 57 0.590545833 5.25 0.7405172119 6.3717334
## 58 0.590545833 5.75 1.3310630451 7.1431284
## 59 0.590545833 6.25 1.9216088784 8.5798171
## 60 0.590545833 6.75 2.5121547116 9.8257600
## 61 -0.164927819 4.75 0.2771079919 4.8714440
## 62 -0.164927819 5.25 0.1121801730 5.6018034
## 63 -0.164927819 5.75 -0.0527476459 4.9358442
## 64 -0.164927819 6.25 -0.2176754648 6.3066977
## 65 -0.164927819 6.75 -0.3826032836 7.8785464
## 66 -0.516256702 4.00 -0.4194741137 3.1959424
## 67 -0.516256702 4.50 -0.9357308158 2.1073866
## 68 -0.516256702 5.00 -1.4519875178 2.6610059
## 69 -0.516256702 5.50 -1.9682442198 2.9622863
## 70 -0.516256702 6.00 -2.4845009218 4.8856468
## 71 -0.150578738 4.00 -0.0497613747 4.1911509
## 72 -0.150578738 4.50 -0.2003401126 4.1395690
## 73 -0.150578738 5.00 -0.3509188505 4.6421490
## 74 -0.150578738 5.50 -0.5014975883 4.3770394
## 75 -0.150578738 6.00 -0.6520763262 4.7584382
## 76 -0.228079192 4.00 0.0120742941 5.3192547
## 77 -0.228079192 4.50 -0.2160048977 5.6320090
## 78 -0.228079192 5.00 -0.4440840895 4.7945085
## 79 -0.228079192 5.50 -0.6721632813 4.6025125
## 80 -0.228079192 6.00 -0.9002424730 6.2906646
## 81 1.100180133 4.75 -0.0504042583 3.6121423
## 82 1.100180133 5.25 1.0497758747 6.0007203
## 83 1.100180133 5.75 2.1499560077 7.1594805
## 84 1.100180133 6.25 3.2501361407 8.8700914
## 85 1.100180133 6.75 4.3503162738 11.9569707
## 86 -0.125461027 4.75 -0.5774882278 3.1904158
## 87 -0.125461027 5.25 -0.7029492547 4.3938467
## 88 -0.125461027 5.75 -0.8284102817 4.8242316
## 89 -0.125461027 6.25 -0.9538713087 5.6005877
## 90 -0.125461027 6.75 -1.0793323356 5.0867403
## 91 -0.079968727 4.00 -0.2570927557 4.1769947
## 92 -0.079968727 4.50 -0.3370614825 3.4018564
## 93 -0.079968727 5.00 -0.4170302093 4.7522504
## 94 -0.079968727 5.50 -0.4969989361 5.3339885
## 95 -0.079968727 6.00 -0.5769676629 5.0232762
## 96 0.447154878 4.75 -0.0147902241 5.3483760
## 97 0.447154878 5.25 0.4323646535 6.8370926
## 98 0.447154878 5.75 0.8795195311 8.6464604
## 99 0.447154878 6.25 1.3266744087 7.7536949
## 100 0.447154878 6.75 1.7738292863 7.9037903
## 101 -0.536938938 4.00 0.1081007956 4.7620138
## 102 -0.536938938 4.50 -0.4288381421 5.2480128
## 103 -0.536938938 5.00 -0.9657770798 3.5168460
## 104 -0.536938938 5.50 -1.5027160176 4.7152841
## 105 -0.536938938 6.00 -2.0396549553 4.6773282
## 106 -0.265432531 4.00 0.2760023029 4.5876575
## 107 -0.265432531 4.50 0.0105697715 5.7712285
## 108 -0.265432531 5.00 -0.2548627598 5.3381067
## 109 -0.265432531 5.50 -0.5202952911 4.1558556
## 110 -0.265432531 6.00 -0.7857278224 4.4501088
## 111 -0.224306401 4.75 -0.2618105207 4.8618029
## 112 -0.224306401 5.25 -0.4861169220 3.4962389
## 113 -0.224306401 5.75 -0.7104233234 5.9337494
## 114 -0.224306401 6.25 -0.9347297248 3.0163440
## 115 -0.224306401 6.75 -1.1590361261 4.9759820
## 116 0.394922440 4.00 -0.3130835595 2.3565272
## 117 0.394922440 4.50 0.0818388805 4.7076711
## 118 0.394922440 5.00 0.4767613204 6.5000894
## 119 0.394922440 5.50 0.8716837604 6.6070305
## 120 0.394922440 6.00 1.2666062003 7.5054387
## 121 0.117739245 4.75 0.5504060007 4.6565175
## 122 0.117739245 5.25 0.6681452455 6.8844125
## 123 0.117739245 5.75 0.7858844904 6.2961207
## 124 0.117739245 6.25 0.9036237352 7.0486200
## 125 0.117739245 6.75 1.0213629800 6.9072631
## 126 -0.324484534 4.00 0.1443355633 3.2285148
## 127 -0.324484534 4.50 -0.1801489712 4.3686836
## 128 -0.324484534 5.00 -0.5046335057 6.2465625
## 129 -0.324484534 5.50 -0.8291180402 4.8441247
## 130 -0.324484534 6.00 -1.1536025746 3.7559090
## 131 -0.776915919 4.75 0.4295187564 4.7278433
## 132 -0.776915919 5.25 -0.3473971624 5.2613983
## 133 -0.776915919 5.75 -1.1243130811 5.9525582
## 134 -0.776915919 6.25 -1.9012289999 5.8590343
## 135 -0.776915919 6.75 -2.6781449186 5.2541459
## 136 0.173654532 4.00 0.4105793692 5.3417046
## 137 0.173654532 4.50 0.5842339014 4.2369078
## 138 0.173654532 5.00 0.7578884336 5.0512849
## 139 0.173654532 5.50 0.9315429658 5.9881550
## 140 0.173654532 6.00 1.1051974980 8.4185116
## 141 0.732180392 4.00 -0.4682032871 4.6399633
## 142 0.732180392 4.50 0.2639771054 4.9253093
## 143 0.732180392 5.00 0.9961574978 6.2179495
## 144 0.732180392 5.50 1.7283378903 7.4608302
## 145 0.732180392 6.00 2.4605182828 7.0095167
## 146 -0.427763883 4.75 -0.0876660750 7.6624980
## 147 -0.427763883 5.25 -0.5154299580 3.8367904
## 148 -0.427763883 5.75 -0.9431938409 3.8248280
## 149 -0.427763883 6.25 -1.3709577239 2.6406496
## 150 -0.427763883 6.75 -1.7987216069 5.9543728
## 151 0.097849571 4.75 0.4169706024 5.8925699
## 152 0.097849571 5.25 0.5148201737 4.8723929
## 153 0.097849571 5.75 0.6126697450 6.2750211
## 154 0.097849571 6.25 0.7105193163 7.8318369
## 155 0.097849571 6.75 0.8083688876 6.3849948
## 156 -0.103982772 4.00 0.2889382421 5.1293489
## 157 -0.103982772 4.50 0.1849554697 4.6927509
## 158 -0.103982772 5.00 0.0809726974 4.9019343
## 159 -0.103982772 5.50 -0.0230100749 5.6079508
## 160 -0.103982772 6.00 -0.1269928472 7.0502048
## 161 -0.819947848 4.00 -0.1496469536 2.3510994
## 162 -0.819947848 4.50 -0.9695948011 4.3387344
## 163 -0.819947848 5.00 -1.7895426487 2.7047674
## 164 -0.819947848 5.50 -2.6094904963 2.9138782
## 165 -0.819947848 6.00 -3.4294383438 1.2521603
## 166 -0.475162687 4.00 -0.2520011616 3.6413620
## 167 -0.475162687 4.50 -0.7271638490 4.2431609
## 168 -0.475162687 5.00 -1.2023265363 2.8764385
## 169 -0.475162687 5.50 -1.6774892237 3.2059066
## 170 -0.475162687 6.00 -2.1526519111 4.0218235
## 171 -0.622533886 4.75 0.1234582831 5.3103538
## 172 -0.622533886 5.25 -0.4990756034 6.1705800
## 173 -0.622533886 5.75 -1.1216094899 4.3974594
## 174 -0.622533886 6.25 -1.7441433764 4.3716371
## 175 -0.622533886 6.75 -2.3666772629 4.2646289
## 176 -0.093796411 4.75 0.5043293035 5.4785337
## 177 -0.093796411 5.25 0.4105328926 8.2032611
## 178 -0.093796411 5.75 0.3167364817 5.6497758
## 179 -0.093796411 6.25 0.2229400708 6.3185409
## 180 -0.093796411 6.75 0.1291436599 8.4598523
## 181 -0.438574102 4.75 -0.4071628176 3.5921318
## 182 -0.438574102 5.25 -0.8457369193 4.5135759
## 183 -0.438574102 5.75 -1.2843110211 3.8162136
## 184 -0.438574102 6.25 -1.7228851228 3.4150200
## 185 -0.438574102 6.75 -2.1614592245 4.7070642
## 186 0.602819989 4.00 0.1192955203 4.0959386
## 187 0.602819989 4.50 0.7221155095 7.0582535
## 188 0.602819989 5.00 1.3249354988 6.6583281
## 189 0.602819989 5.50 1.9277554881 7.1711173
## 190 0.602819989 6.00 2.5305754774 8.9889838
## 191 -0.399817566 4.75 -0.2056909515 4.8328585
## 192 -0.399817566 5.25 -0.6055085175 4.8095480
## 193 -0.399817566 5.75 -1.0053260835 4.2371058
## 194 -0.399817566 6.25 -1.4051436495 3.9781762
## 195 -0.399817566 6.75 -1.8049612155 6.3416895
## 196 -0.110267479 4.75 -0.2071113490 6.9585118
## 197 -0.110267479 5.25 -0.3173788276 5.2048423
## 198 -0.110267479 5.75 -0.4276463062 5.6729383
## 199 -0.110267479 6.25 -0.5379137848 6.1777098
## 200 -0.110267479 6.75 -0.6481812634 5.7422939
## 201 0.391262107 4.75 -0.0925769120 4.3478345
## 202 0.391262107 5.25 0.2986851952 4.2891642
## 203 0.391262107 5.75 0.6899473024 5.9520661
## 204 0.391262107 6.25 1.0812094096 7.9940799
## 205 0.391262107 6.75 1.4724715168 7.2318249
## 206 0.399440733 4.00 -0.7939913341 2.3922436
## 207 0.399440733 4.50 -0.3945506014 2.7195939
## 208 0.399440733 5.00 0.0048901313 5.3848896
## 209 0.399440733 5.50 0.4043308639 5.5466285
## 210 0.399440733 6.00 0.8037715966 5.8413102
## 211 -0.190640019 4.00 0.2750149628 3.4228468
## 212 -0.190640019 4.50 0.0843749440 5.3711847
## 213 -0.190640019 5.00 -0.1062650747 3.2901053
## 214 -0.190640019 5.50 -0.2969050934 4.3295675
## 215 -0.190640019 6.00 -0.4875451122 6.5699158
## 216 0.214433193 4.00 -0.4744802636 3.2498840
## 217 0.214433193 4.50 -0.2600470711 5.9899569
## 218 0.214433193 5.00 -0.0456138786 3.9128271
## 219 0.214433193 5.50 0.1688193140 6.1515968
## 220 0.214433193 6.00 0.3832525065 5.0650193
## 221 -0.167772260 4.75 -0.3577680737 3.9795893
## 222 -0.167772260 5.25 -0.5255403341 4.3755560
## 223 -0.167772260 5.75 -0.6933125945 4.2520364
## 224 -0.167772260 6.25 -0.8610848549 5.0053661
## 225 -0.167772260 6.75 -1.0288571153 6.5924264
## 226 0.088582010 4.00 0.2411842358 5.0985525
## 227 0.088582010 4.50 0.3297662456 5.4755378
## 228 0.088582010 5.00 0.4183482553 5.9363732
## 229 0.088582010 5.50 0.5069302650 5.3023143
## 230 0.088582010 6.00 0.5955122747 7.1502703
## 231 0.434855020 4.75 0.0046682078 4.1170156
## 232 0.434855020 5.25 0.4395232276 4.9617436
## 233 0.434855020 5.75 0.8743782474 6.0114039
## 234 0.434855020 6.25 1.3092332672 7.9008553
## 235 0.434855020 6.75 1.7440882871 6.8196950
## 236 -1.203117829 4.00 0.6606201758 5.3392578
## 237 -1.203117829 4.50 -0.5424976529 4.8516475
## 238 -1.203117829 5.00 -1.7456154815 3.1855686
## 239 -1.203117829 5.50 -2.9487333102 1.3742092
## 240 -1.203117829 6.00 -4.1518511389 0.3458654
## 241 0.058398991 4.75 1.0351317821 5.9335005
## 242 0.058398991 5.25 1.0935307731 6.3777922
## 243 0.058398991 5.75 1.1519297642 8.0042742
## 244 0.058398991 6.25 1.2103287552 6.5922682
## 245 0.058398991 6.75 1.2687277462 8.2851697
## 246 0.265139310 4.75 -1.0684598899 2.4176369
## 247 0.265139310 5.25 -0.8033205796 3.6148693
## 248 0.265139310 5.75 -0.5381812693 4.1866287
## 249 0.265139310 6.25 -0.2730419590 6.0556829
## 250 0.265139310 6.75 -0.0079026487 6.1502554
## 251 -0.011735327 4.00 0.1766964128 4.5046173
## 252 -0.011735327 4.50 0.1649610859 6.2134771
## 253 -0.011735327 5.00 0.1532257589 4.4704489
## 254 -0.011735327 5.50 0.1414904319 7.3608217
## 255 -0.011735327 6.00 0.1297551049 5.7042281
## 256 -0.110704317 4.00 0.0315776884 4.6948798
## 257 -0.110704317 4.50 -0.0791266290 5.3531867
## 258 -0.110704317 5.00 -0.1898309464 3.7777730
## 259 -0.110704317 5.50 -0.3005352638 5.2253421
## 260 -0.110704317 6.00 -0.4112395812 5.2625496
## 261 0.267247246 4.00 -0.4569550076 3.7249530
## 262 0.267247246 4.50 -0.1897077620 3.3656773
## 263 0.267247246 5.00 0.0775394835 5.0593038
## 264 0.267247246 5.50 0.3447867290 6.2801467
## 265 0.267247246 6.00 0.6120339746 6.0885546
## 266 -1.147996051 4.00 -0.1486992395 4.3341678
## 267 -1.147996051 4.50 -1.2966952906 3.3721710
## 268 -1.147996051 5.00 -2.4446913416 2.0828801
## 269 -1.147996051 5.50 -3.5926873927 0.1968994
## 270 -1.147996051 6.00 -4.7406834438 -0.3490693
## 271 0.636127012 4.75 -0.2452462978 4.4304300
## 272 0.636127012 5.25 0.3908807147 6.7300209
## 273 0.636127012 5.75 1.0270077271 7.6252331
## 274 0.636127012 6.25 1.6631347396 8.3429073
## 275 0.636127012 6.75 2.2992617521 8.3402542
## 276 0.187476808 4.75 -0.0334339143 6.2608927
## 277 0.187476808 5.25 0.1540428934 6.5796132
## 278 0.187476808 5.75 0.3415197010 6.9305696
## 279 0.187476808 6.25 0.5289965087 6.7319674
## 280 0.187476808 6.75 0.7164733164 9.0120183
## 281 0.606205031 4.75 0.0044162876 5.1671545
## 282 0.606205031 5.25 0.6106213181 7.8108154
## 283 0.606205031 5.75 1.2168263487 6.6008662
## 284 0.606205031 6.25 1.8230313793 10.2421591
## 285 0.606205031 6.75 2.4292364098 9.9493955
## 286 0.204446739 4.00 0.3135231090 4.1244072
## 287 0.204446739 4.50 0.5179698484 6.2862652
## 288 0.204446739 5.00 0.7224165877 5.1104960
## 289 0.204446739 5.50 0.9268633270 6.2598105
## 290 0.204446739 6.00 1.1313100664 7.4425937
## 291 -0.117312166 4.00 -0.7144599298 3.3455313
## 292 -0.117312166 4.50 -0.8317720956 2.1532336
## 293 -0.117312166 5.00 -0.9490842614 6.0935377
## 294 -0.117312166 5.50 -1.0663964272 7.1834961
## 295 -0.117312166 6.00 -1.1837085931 5.9570330
## 296 0.273648640 4.00 0.0916712782 4.3572817
## 297 0.273648640 4.50 0.3653199182 6.7575644
## 298 0.273648640 5.00 0.6389685582 6.2443386
## 299 0.273648640 5.50 0.9126171982 5.8160307
## 300 0.273648640 6.00 1.1862658382 7.5081614
## 301 0.274579968 4.00 -0.3799843383 4.2180111
## 302 0.274579968 4.50 -0.1054043700 5.0077919
## 303 0.274579968 5.00 0.1691755983 4.6267556
## 304 0.274579968 5.50 0.4437555666 5.7505636
## 305 0.274579968 6.00 0.7183355349 8.1565042
## 306 -0.085320251 4.00 0.1753612642 3.6834926
## 307 -0.085320251 4.50 0.0900410135 6.1127848
## 308 -0.085320251 5.00 0.0047207628 5.4312436
## 309 -0.085320251 5.50 -0.0805994878 5.6920215
## 310 -0.085320251 6.00 -0.1659197385 8.1855221
## 311 -0.031157458 4.75 0.2506004956 4.7000290
## 312 -0.031157458 5.25 0.2194430381 4.3558398
## 313 -0.031157458 5.75 0.1882855805 4.7452272
## 314 -0.031157458 6.25 0.1571281230 7.9912763
## 315 -0.031157458 6.75 0.1259706655 7.6463498
## 316 1.235779437 4.00 -0.0630647977 5.3014722
## 317 1.235779437 4.50 1.1727146392 5.9838710
## 318 1.235779437 5.00 2.4084940762 7.1736612
## 319 1.235779437 5.50 3.6442735132 8.9232202
## 320 1.235779437 6.00 4.8800529501 11.1389690
## 321 0.209231973 4.00 -0.0348031878 3.6633248
## 322 0.209231973 4.50 0.1744287849 5.6431827
## 323 0.209231973 5.00 0.3836607576 4.2898169
## 324 0.209231973 5.50 0.5928927303 4.9195681
## 325 0.209231973 6.00 0.8021247030 7.0605909
## 326 -0.250513758 4.75 -0.1505143398 6.1770772
## 327 -0.250513758 5.25 -0.4010280983 5.8524360
## 328 -0.250513758 5.75 -0.6515418567 4.1981219
## 329 -0.250513758 6.25 -0.9020556152 3.6356824
## 330 -0.250513758 6.75 -1.1525693736 7.3339311
## 331 0.381479872 4.75 0.0870682155 4.6321558
## 332 0.381479872 5.25 0.4685480877 4.7515365
## 333 0.381479872 5.75 0.8500279598 6.5238776
## 334 0.381479872 6.25 1.2315078320 6.3475238
## 335 0.381479872 6.75 1.6129877041 7.8647138
## 336 0.895008359 4.75 -0.0226480430 4.8235395
## 337 0.895008359 5.25 0.8723603160 5.5543788
## 338 0.895008359 5.75 1.7673686749 8.2994881
## 339 0.895008359 6.25 2.6623770339 10.0331836
## 340 0.895008359 6.75 3.5573853929 12.5723588
## 341 -0.372069500 4.00 0.0768848517 3.4393725
## 342 -0.372069500 4.50 -0.2951846479 4.8493842
## 343 -0.372069500 5.00 -0.6672541475 2.5877310
## 344 -0.372069500 5.50 -1.0393236471 6.0789441
## 345 -0.372069500 6.00 -1.4113931466 4.7707754
## 346 -0.247944278 4.75 0.0093018100 3.2351883
## 347 -0.247944278 5.25 -0.2386424681 4.8488319
## 348 -0.247944278 5.75 -0.4865867462 5.1057365
## 349 -0.247944278 6.25 -0.7345310243 6.2532075
## 350 -0.247944278 6.75 -0.9824753024 6.8123498
## 351 0.729998798 4.75 0.5388003677 4.2006038
## 352 0.729998798 5.25 1.2687991656 6.6897947
## 353 0.729998798 5.75 1.9987979636 6.8309414
## 354 0.729998798 6.25 2.7287967615 8.3366246
## 355 0.729998798 6.75 3.4587955594 10.3718239
## 356 0.238970105 4.00 -0.6866928998 3.2020100
## 357 0.238970105 4.50 -0.4477227946 3.0158150
## 358 0.238970105 5.00 -0.2087526895 4.9347945
## 359 0.238970105 5.50 0.0302174156 6.6041304
## 360 0.238970105 6.00 0.2691875208 6.8985717
## 361 0.334387193 4.00 0.3943469930 3.8922268
## 362 0.334387193 4.50 0.7287341856 4.8662596
## 363 0.334387193 5.00 1.0631213783 6.7925095
## 364 0.334387193 5.50 1.3975085710 6.6547672
## 365 0.334387193 6.00 1.7318957636 8.0119699
## 366 1.015668805 4.00 0.3350808339 5.1435289
## 367 1.015668805 4.50 1.3507496391 4.6389587
## 368 1.015668805 5.00 2.3664184443 8.1705696
## 369 1.015668805 5.50 3.3820872496 7.9968083
## 370 1.015668805 6.00 4.3977560548 9.7865827
## 371 0.365590596 4.75 0.0460618896 2.9616345
## 372 0.365590596 5.25 0.4116524853 5.4005923
## 373 0.365590596 5.75 0.7772430811 7.2135656
## 374 0.365590596 6.25 1.1428336769 7.2637635
## 375 0.365590596 6.75 1.5084242726 7.6597696
## 376 0.643083929 4.75 -0.3004007765 2.4756646
## 377 0.643083929 5.25 0.3426831528 6.1741656
## 378 0.643083929 5.75 0.9857670820 5.8445824
## 379 0.643083929 6.25 1.6288510112 8.5885357
## 380 0.643083929 6.75 2.2719349404 8.7404135
## 381 -0.426052918 4.75 0.1628493630 4.6566172
## 382 -0.426052918 5.25 -0.2632035547 4.7849407
## 383 -0.426052918 5.75 -0.6892564725 5.1077678
## 384 -0.426052918 6.25 -1.1153093903 6.6990538
## 385 -0.426052918 6.75 -1.5413623081 7.3634897
## 386 0.616478388 4.00 -0.0603501439 4.7437296
## 387 0.616478388 4.50 0.5561282439 6.8102872
## 388 0.616478388 5.00 1.1726066317 8.1020683
## 389 0.616478388 5.50 1.7890850195 7.0815856
## 390 0.616478388 6.00 2.4055634073 9.6715329
## 391 -0.195501511 4.00 0.3053801701 4.4968166
## 392 -0.195501511 4.50 0.1098786586 2.7135722
## 393 -0.195501511 5.00 -0.0856228528 4.2142279
## 394 -0.195501511 5.50 -0.2811243643 3.1155664
## 395 -0.195501511 6.00 -0.4766258757 5.0041051
## 396 -0.702506154 4.00 -0.1614793939 5.0010355
## 397 -0.702506154 4.50 -0.8639855474 3.8726416
## 398 -0.702506154 5.00 -1.5664917010 2.9936996
## 399 -0.702506154 5.50 -2.2689978546 3.4111826
## 400 -0.702506154 6.00 -2.9715040081 3.0892037
## 401 0.251305134 4.75 0.7243524605 4.1970289
## 402 0.251305134 5.25 0.9756575948 5.1439260
## 403 0.251305134 5.75 1.2269627290 7.2100120
## 404 0.251305134 6.25 1.4782678633 7.3871237
## 405 0.251305134 6.75 1.7295729975 6.8193397
## 406 0.705297026 4.75 -0.3588862990 4.6821953
## 407 0.705297026 5.25 0.3464107267 6.3938957
## 408 0.705297026 5.75 1.0517077524 7.0794472
## 409 0.705297026 6.25 1.7570047781 6.6034222
## 410 0.705297026 6.75 2.4623018038 9.4480057
## 411 -0.022394105 4.00 -0.1256226964 3.6362802
## 412 -0.022394105 4.50 -0.1480168011 4.0640264
## 413 -0.022394105 5.00 -0.1704109058 4.6883279
## 414 -0.022394105 5.50 -0.1928050106 4.9854923
## 415 -0.022394105 6.00 -0.2151991153 5.9810536
## 416 -0.311036046 4.00 0.1309306728 3.8766376
## 417 -0.311036046 4.50 -0.1801053734 4.7586956
## 418 -0.311036046 5.00 -0.4911414196 4.1492234
## 419 -0.311036046 5.50 -0.8021774658 3.3210575
## 420 -0.311036046 6.00 -1.1132135120 4.8766188
## 421 -0.856242160 4.75 -0.5188003841 4.5462121
## 422 -0.856242160 5.25 -1.3750425440 3.4565848
## 423 -0.856242160 5.75 -2.2312847040 2.9457021
## 424 -0.856242160 6.25 -3.0875268639 2.8186561
## 425 -0.856242160 6.75 -3.9437690239 3.7018456
## 426 -0.236447169 4.75 -0.3495057343 1.6710880
## 427 -0.236447169 5.25 -0.5859529033 4.6007124
## 428 -0.236447169 5.75 -0.8224000724 3.0529984
## 429 -0.236447169 6.25 -1.0588472414 6.1360529
## 430 -0.236447169 6.75 -1.2952944104 6.2887688
## 431 0.812390234 4.75 -0.5815894301 4.3198507
## 432 0.812390234 5.25 0.2308008040 5.3178946
## 433 0.812390234 5.75 1.0431910382 6.6993523
## 434 0.812390234 6.25 1.8555812724 7.9438060
## 435 0.812390234 6.75 2.6679715066 11.1875510
## 436 -0.236241312 4.00 -0.2061567618 3.8058452
## 437 -0.236241312 4.50 -0.4423980742 3.6563671
## 438 -0.236241312 5.00 -0.6786393866 4.9710521
## 439 -0.236241312 5.50 -0.9148806989 4.3038694
## 440 -0.236241312 6.00 -1.1511220113 8.0529283
## 441 -0.098423038 4.00 -0.0325797514 5.0474074
## 442 -0.098423038 4.50 -0.1310027896 5.2321019
## 443 -0.098423038 5.00 -0.2294258278 5.3516438
## 444 -0.098423038 5.50 -0.3278488660 5.0308346
## 445 -0.098423038 6.00 -0.4262719042 4.4396478
## 446 -0.465739465 4.00 -0.2265049785 5.1123209
## 447 -0.465739465 4.50 -0.6922444437 2.1511511
## 448 -0.465739465 5.00 -1.1579839089 5.5804340
## 449 -0.465739465 5.50 -1.6237233741 3.7089667
## 450 -0.465739465 6.00 -2.0894628393 2.8114485
## 451 -1.449302685 4.75 0.3113733881 6.0796797
## 452 -1.449302685 5.25 -1.1379292972 2.8038548
## 453 -1.449302685 5.75 -2.5872319824 5.0973134
## 454 -1.449302685 6.25 -4.0365346676 1.8520246
## 455 -1.449302685 6.75 -5.4858373528 0.7775726
## 456 0.144476894 4.00 -0.0968864750 2.4575197
## 457 0.144476894 4.50 0.0475904192 4.9111952
## 458 0.144476894 5.00 0.1920673134 4.5178921
## 459 0.144476894 5.50 0.3365442076 6.0981555
## 460 0.144476894 6.00 0.4810211018 5.4340510
## 461 0.109145241 4.00 -0.1995531754 5.6715432
## 462 0.109145241 4.50 -0.0904079343 4.6789790
## 463 0.109145241 5.00 0.0187373068 5.1186843
## 464 0.109145241 5.50 0.1278825479 8.1829680
## 465 0.109145241 6.00 0.2370277890 6.0876302
## 466 0.461376520 4.75 -0.1704870715 4.5932984
## 467 0.461376520 5.25 0.2908894483 6.1563313
## 468 0.461376520 5.75 0.7522659680 5.9219594
## 469 0.461376520 6.25 1.2136424878 8.2949772
## 470 0.461376520 6.75 1.6750190076 10.3365741
## 471 0.434428853 4.00 0.7161094414 3.9848999
## 472 0.434428853 4.50 1.1505382943 5.3228771
## 473 0.434428853 5.00 1.5849671471 5.9732942
## 474 0.434428853 5.50 2.0193959999 9.1166754
## 475 0.434428853 6.00 2.4538248528 8.1774276
## 476 0.270269185 4.00 -0.5217097578 3.0218931
## 477 0.270269185 4.50 -0.2514405730 6.6362077
## 478 0.270269185 5.00 0.0188286119 5.1677420
## 479 0.270269185 5.50 0.2890977967 6.5833753
## 480 0.270269185 6.00 0.5593669815 7.1819124
## 481 -0.365346163 4.75 -0.2725569989 5.5251699
## 482 -0.365346163 5.25 -0.6379031620 5.8507503
## 483 -0.365346163 5.75 -1.0032493251 5.6198547
## 484 -0.365346163 6.25 -1.3685954881 5.5443587
## 485 -0.365346163 6.75 -1.7339416512 3.7968290
## 486 1.048480901 4.00 0.0873010275 5.2912406
## 487 1.048480901 4.50 1.1357819290 3.8581927
## 488 1.048480901 5.00 2.1842628305 6.2357213
## 489 1.048480901 5.50 3.2327437320 8.7755025
## 490 1.048480901 6.00 4.2812246335 11.5375821
## 491 1.214815652 4.00 0.1244798960 3.6444950
## 492 1.214815652 4.50 1.3392955484 7.1034582
## 493 1.214815652 5.00 2.5541112008 6.8090606
## 494 1.214815652 5.50 3.7689268532 9.2825453
## 495 1.214815652 6.00 4.9837425056 10.6569018
## 496 0.053483573 4.75 0.1610392925 3.0853393
## 497 0.053483573 5.25 0.2145228657 4.8453592
## 498 0.053483573 5.75 0.2680064390 6.2320134
## 499 0.053483573 6.25 0.3214900122 6.4154924
## 500 0.053483573 6.75 0.3749735854 8.5637472
## 501 -0.165445205 4.00 0.0728889840 4.3023968
## 502 -0.165445205 4.50 -0.0925562214 4.2673682
## 503 -0.165445205 5.00 -0.2580014267 5.7762963
## 504 -0.165445205 5.50 -0.4234466321 5.4335484
## 505 -0.165445205 6.00 -0.5888918375 5.1818662
## 506 -0.341946340 4.75 0.4289182542 5.6232659
## 507 -0.341946340 5.25 0.0869719138 7.7014481
## 508 -0.341946340 5.75 -0.2549744266 4.8798544
## 509 -0.341946340 6.25 -0.5969207669 5.2466936
## 510 -0.341946340 6.75 -0.9388671073 6.0825517
## 511 0.178406504 4.00 0.2088824406 3.6471120
## 512 0.178406504 4.50 0.3872889442 4.3965796
## 513 0.178406504 5.00 0.5656954478 6.0770452
## 514 0.178406504 5.50 0.7441019514 5.2280225
## 515 0.178406504 6.00 0.9225084550 7.1270896
## 516 0.476519984 4.75 -0.3188911973 3.1856624
## 517 0.476519984 5.25 0.1576287864 3.8875778
## 518 0.476519984 5.75 0.6341487701 6.1380641
## 519 0.476519984 6.25 1.1106687537 7.4756559
## 520 0.476519984 6.75 1.5871887374 9.6306498
## 521 0.560744018 4.00 0.3994620521 4.2420735
## 522 0.560744018 4.50 0.9602060698 5.2745612
## 523 0.560744018 5.00 1.5209500875 6.1482448
## 524 0.560744018 5.50 2.0816941052 6.8004596
## 525 0.560744018 6.00 2.6424381228 8.1610691
## 526 -0.321193375 4.75 -0.0738738573 5.0410702
## 527 -0.321193375 5.25 -0.3950672324 3.8536279
## 528 -0.321193375 5.75 -0.7162606076 5.0708737
## 529 -0.321193375 6.25 -1.0374539828 4.4748474
## 530 -0.321193375 6.75 -1.3586473580 4.1643567
## 531 -0.014836283 4.75 -0.0459384504 5.2458387
## 532 -0.014836283 5.25 -0.0607747334 5.4588763
## 533 -0.014836283 5.75 -0.0756110163 6.4078155
## 534 -0.014836283 6.25 -0.0904472993 6.8694372
## 535 -0.014836283 6.75 -0.1052835822 6.2991214
## 536 -0.936101367 4.75 0.1733455410 3.6994047
## 537 -0.936101367 5.25 -0.7627558262 3.5986775
## 538 -0.936101367 5.75 -1.6988571934 5.7938048
## 539 -0.936101367 6.25 -2.6349585606 3.7394816
## 540 -0.936101367 6.75 -3.5710599278 4.0779259
## 541 0.039345482 4.75 0.1361683630 5.7626480
## 542 0.039345482 5.25 0.1755138447 5.2340759
## 543 0.039345482 5.75 0.2148593265 5.6717608
## 544 0.039345482 6.25 0.2542048082 6.2660348
## 545 0.039345482 6.75 0.2935502899 6.2055645
## 546 -0.497584248 4.00 -0.1809533430 2.4557895
## 547 -0.497584248 4.50 -0.6785375909 3.8225152
## 548 -0.497584248 5.00 -1.1761218389 5.0107306
## 549 -0.497584248 5.50 -1.6737060868 3.1574081
## 550 -0.497584248 6.00 -2.1712903347 3.1771781
## 551 0.991930705 4.00 0.2132662725 3.8648500
## 552 0.991930705 4.50 1.2051969775 4.4021449
## 553 0.991930705 5.00 2.1971276825 7.1226871
## 554 0.991930705 5.50 3.1890583875 8.2364794
## 555 0.991930705 6.00 4.1809890925 11.0735574
## 556 0.099374024 4.00 -0.8545692831 5.1062023
## 557 0.099374024 4.50 -0.7551952593 4.8309145
## 558 0.099374024 5.00 -0.6558212354 3.4443486
## 559 0.099374024 5.50 -0.5564472115 2.6765061
## 560 0.099374024 6.00 -0.4570731877 5.0055365
## 561 -0.384629881 4.75 -0.6581641424 4.2445317
## 562 -0.384629881 5.25 -1.0427940235 4.1302953
## 563 -0.384629881 5.75 -1.4274239045 4.5898962
## 564 -0.384629881 6.25 -1.8120537856 4.6903237
## 565 -0.384629881 6.75 -2.1966836667 3.3002295
## 566 -1.034649250 4.75 0.0416565290 3.3325434
## 567 -1.034649250 5.25 -0.9929927211 4.3649885
## 568 -1.034649250 5.75 -2.0276419712 3.5759213
## 569 -1.034649250 6.25 -3.0622912213 2.5208355
## 570 -1.034649250 6.75 -4.0969404714 2.6888907
## 571 -0.576353446 4.75 0.5490726237 6.1081287
## 572 -0.576353446 5.25 -0.0272808226 6.7064386
## 573 -0.576353446 5.75 -0.6036342689 5.8396210
## 574 -0.576353446 6.25 -1.1799877151 5.1024089
## 575 -0.576353446 6.75 -1.7563411614 5.7207056
## 576 0.617928553 4.00 -0.5217662994 2.5637002
## 577 0.617928553 4.50 0.0961622534 3.4268060
## 578 0.617928553 5.00 0.7140908063 6.2774191
## 579 0.617928553 5.50 1.3320193592 7.4707491
## 580 0.617928553 6.00 1.9499479121 7.6514859
## 581 -0.046432696 4.00 0.4276516832 4.6117646
## 582 -0.046432696 4.50 0.3812189868 6.1599102
## 583 -0.046432696 5.00 0.3347862903 6.4067077
## 584 -0.046432696 5.50 0.2883535939 6.1926301
## 585 -0.046432696 6.00 0.2419208975 6.9795962
## 586 0.563024730 4.00 -0.6868796670 3.3930203
## 587 0.563024730 4.50 -0.1238549366 3.6498050
## 588 0.563024730 5.00 0.4391697938 5.5217449
## 589 0.563024730 5.50 1.0021945242 8.0117998
## 590 0.563024730 6.00 1.5652192546 7.5509761
## 591 0.163809942 4.75 0.4202201203 6.5449199
## 592 0.163809942 5.25 0.5840300621 5.4012652
## 593 0.163809942 5.75 0.7478400039 8.1996131
## 594 0.163809942 6.25 0.9116499457 6.9506290
## 595 0.163809942 6.75 1.0754598874 8.4013510
## 596 0.049650461 4.75 0.0310440576 3.7935886
## 597 0.049650461 5.25 0.0806945190 5.5483770
## 598 0.049650461 5.75 0.1303449804 6.2211083
## 599 0.049650461 6.25 0.1799954417 7.3855569
## 600 0.049650461 6.75 0.2296459031 7.2164529
## 601 0.325687389 4.75 -0.0124182216 4.1016729
## 602 0.325687389 5.25 0.3132691669 5.0319511
## 603 0.325687389 5.75 0.6389565554 4.8753734
## 604 0.325687389 6.25 0.9646439440 8.8526029
## 605 0.325687389 6.75 1.2903313325 8.4178785
## 606 -0.142959317 4.75 0.5488962828 3.7420024
## 607 -0.142959317 5.25 0.4059369658 6.4300677
## 608 -0.142959317 5.75 0.2629776489 6.1888414
## 609 -0.142959317 6.25 0.1200183319 5.3541857
## 610 -0.142959317 6.75 -0.0229409850 6.8810222
## 611 -0.423748951 4.75 -0.2363313217 4.9732438
## 612 -0.423748951 5.25 -0.6600802731 3.6691829
## 613 -0.423748951 5.75 -1.0838292246 6.3097755
## 614 -0.423748951 6.25 -1.5075781760 3.3310446
## 615 -0.423748951 6.75 -1.9313271274 5.0845487
## 616 -0.598178355 4.00 -0.7294582828 3.5167421
## 617 -0.598178355 4.50 -1.3276366382 1.5280974
## 618 -0.598178355 5.00 -1.9258149936 2.4253489
## 619 -0.598178355 5.50 -2.5239933491 1.4950837
## 620 -0.598178355 6.00 -3.1221717045 3.1908956
## 621 0.853317222 4.00 0.7889883584 5.9190126
## 622 0.853317222 4.50 1.6423055800 5.6031513
## 623 0.853317222 5.00 2.4956228016 9.0190789
## 624 0.853317222 5.50 3.3489400232 7.8230714
## 625 0.853317222 6.00 4.2022572448 11.2795710
## 626 -0.552003178 4.00 -0.3488158086 3.7387406
## 627 -0.552003178 4.50 -0.9008189869 2.7802218
## 628 -0.552003178 5.00 -1.4528221653 2.6111321
## 629 -0.552003178 5.50 -2.0048253437 1.9820581
## 630 -0.552003178 6.00 -2.5568285221 4.8879912
## 631 -0.245141330 4.75 -0.2030661992 3.7888660
## 632 -0.245141330 5.25 -0.4482075291 1.8016562
## 633 -0.245141330 5.75 -0.6933488590 7.0249412
## 634 -0.245141330 6.25 -0.9384901889 5.4032408
## 635 -0.245141330 6.75 -1.1836315188 5.4425761
## 636 0.037202363 4.00 -0.0515035951 6.2612390
## 637 0.037202363 4.50 -0.0143012320 4.6753623
## 638 0.037202363 5.00 0.0229011312 5.2549641
## 639 0.037202363 5.50 0.0601034944 5.5594987
## 640 0.037202363 6.00 0.0973058575 6.6146900
## 641 -1.813351553 4.75 0.0007636665 4.8625654
## 642 -1.813351553 5.25 -1.8125878862 3.9095070
## 643 -1.813351553 5.75 -3.6259394389 1.5712898
## 644 -1.813351553 6.25 -5.4392909916 0.9136403
## 645 -1.813351553 6.75 -7.2526425443 -0.4884260
## 646 0.348772113 4.00 -0.3974371652 4.4625990
## 647 0.348772113 4.50 -0.0486650520 3.8876063
## 648 0.348772113 5.00 0.3001070612 4.9658374
## 649 0.348772113 5.50 0.6488791743 4.6731159
## 650 0.348772113 6.00 0.9976512875 7.5884259
## 651 0.058540458 4.00 0.0089879637 4.7423881
## 652 0.058540458 4.50 0.0675284217 5.6816206
## 653 0.058540458 5.00 0.1260688796 3.6529089
## 654 0.058540458 5.50 0.1846093375 6.5687483
## 655 0.058540458 6.00 0.2431497955 5.3795697
## 656 0.424315091 4.75 -0.1111166440 4.4253535
## 657 0.424315091 5.25 0.3131984469 4.3733773
## 658 0.424315091 5.75 0.7375135379 5.1877106
## 659 0.424315091 6.25 1.1618286288 7.2360211
## 660 0.424315091 6.75 1.5861437198 7.3820094
## 661 -0.225556240 4.75 1.1036178064 6.0053095
## 662 -0.225556240 5.25 0.8780615667 5.3832472
## 663 -0.225556240 5.75 0.6525053270 6.8462391
## 664 -0.225556240 6.25 0.4269490872 5.0798896
## 665 -0.225556240 6.75 0.2013928475 6.2807819
## 666 -0.590959802 4.75 -0.3088163046 3.0955999
## 667 -0.590959802 5.25 -0.8997761064 4.7334026
## 668 -0.590959802 5.75 -1.4907359082 4.5201484
## 669 -0.590959802 6.25 -2.0816957100 5.0635804
## 670 -0.590959802 6.75 -2.6726555118 4.2847942
## 671 -0.700727458 4.75 -0.4978073826 4.7511909
## 672 -0.700727458 5.25 -1.1985348407 3.7611839
## 673 -0.700727458 5.75 -1.8992622988 2.3177551
## 674 -0.700727458 6.25 -2.5999897570 4.4865570
## 675 -0.700727458 6.75 -3.3007172151 3.0240234
## 676 0.634115192 4.00 -0.0005776546 3.9804179
## 677 0.634115192 4.50 0.6335375370 5.7260631
## 678 0.634115192 5.00 1.2676527285 6.4290587
## 679 0.634115192 5.50 1.9017679201 6.0465373
## 680 0.634115192 6.00 2.5358831117 6.5994357
## 681 -0.339766521 4.75 -0.5407720858 3.8207744
## 682 -0.339766521 5.25 -0.8805386068 4.1890755
## 683 -0.339766521 5.75 -1.2203051279 4.5796665
## 684 -0.339766521 6.25 -1.5600716489 5.0799072
## 685 -0.339766521 6.75 -1.8998381700 5.1255055
## 686 1.398097178 4.75 -0.3838561011 5.3306906
## 687 1.398097178 5.25 1.0142410767 5.3664421
## 688 1.398097178 5.75 2.4123382545 7.5098556
## 689 1.398097178 6.25 3.8104354323 10.5952843
## 690 1.398097178 6.75 5.2085326101 13.5411799
## 691 0.440822147 4.00 0.2748297395 4.2723973
## 692 0.440822147 4.50 0.7156518865 4.9662790
## 693 0.440822147 5.00 1.1564740335 6.1845499
## 694 0.440822147 5.50 1.5972961805 7.2785092
## 695 0.440822147 6.00 2.0381183275 8.6159284
## 696 -0.134538336 4.75 0.3566071496 4.9955894
## 697 -0.134538336 5.25 0.2220688136 3.8819254
## 698 -0.134538336 5.75 0.0875304777 6.3135152
## 699 -0.134538336 6.25 -0.0470078583 4.5255116
## 700 -0.134538336 6.75 -0.1815461942 7.5463486
## 701 0.808855369 4.75 0.2784570433 4.9256792
## 702 0.808855369 5.25 1.0873124127 5.0828871
## 703 0.808855369 5.75 1.8961677820 7.5386209
## 704 0.808855369 6.25 2.7050231514 8.5878095
## 705 0.808855369 6.75 3.5138785207 10.4689607
## 706 0.045869956 4.75 0.1968523922 4.3877353
## 707 0.045869956 5.25 0.2427223481 4.4845347
## 708 0.045869956 5.75 0.2885923040 5.9256673
## 709 0.045869956 6.25 0.3344622599 6.5757001
## 710 0.045869956 6.75 0.3803322157 3.8141509
## 711 -0.708804690 4.00 -0.4897879902 3.9476200
## 712 -0.708804690 4.50 -1.1985926806 3.4546246
## 713 -0.708804690 5.00 -1.9073973709 2.9325455
## 714 -0.708804690 5.50 -2.6162020613 2.5519312
## 715 -0.708804690 6.00 -3.3250067516 2.1278356
## 716 -0.461487929 4.00 0.0625239738 5.4191020
## 717 -0.461487929 4.50 -0.3989639556 4.4813278
## 718 -0.461487929 5.00 -0.8604518851 4.4054361
## 719 -0.461487929 5.50 -1.3219398145 2.7702589
## 720 -0.461487929 6.00 -1.7834277439 3.3233179
## 721 -0.127775926 4.75 0.2062427620 5.9139971
## 722 -0.127775926 5.25 0.0784668361 5.0074861
## 723 -0.127775926 5.75 -0.0493090897 6.8062268
## 724 -0.127775926 6.25 -0.1770850156 4.7033935
## 725 -0.127775926 6.75 -0.3048609415 7.1075287
## 726 0.179133996 4.75 -0.3938877205 5.2355869
## 727 0.179133996 5.25 -0.2147537249 4.4854738
## 728 0.179133996 5.75 -0.0356197294 6.1803006
## 729 0.179133996 6.25 0.1435142662 6.5643637
## 730 0.179133996 6.75 0.3226482618 8.7868726
## 731 -0.091103353 4.75 0.2916461306 5.4102527
## 732 -0.091103353 5.25 0.2005427776 5.4265756
## 733 -0.091103353 5.75 0.1094394245 5.4909420
## 734 -0.091103353 6.25 0.0183360715 7.5744525
## 735 -0.091103353 6.75 -0.0727672816 5.9318404
## 736 -0.476532287 4.00 -0.0265658650 4.6999046
## 737 -0.476532287 4.50 -0.5030981525 3.2786463
## 738 -0.476532287 5.00 -0.9796304400 4.2796773
## 739 -0.476532287 5.50 -1.4561627274 5.1015680
## 740 -0.476532287 6.00 -1.9326950149 6.8442186
## 741 -0.390009335 4.75 -0.1640778212 7.0240065
## 742 -0.390009335 5.25 -0.5540871557 5.2202500
## 743 -0.390009335 5.75 -0.9440964902 5.1781096
## 744 -0.390009335 6.25 -1.3341058247 5.8455953
## 745 -0.390009335 6.75 -1.7241151592 5.8917805
## 746 -0.373486901 4.00 -1.1308971068 4.0449999
## 747 -0.373486901 4.50 -1.5043840080 3.0722460
## 748 -0.373486901 5.00 -1.8778709092 2.4156498
## 749 -0.373486901 5.50 -2.2513578104 2.9540205
## 750 -0.373486901 6.00 -2.6248447116 4.4624389
## 751 -0.300065783 4.00 -0.3364280213 3.0960561
## 752 -0.300065783 4.50 -0.6364938048 3.8218324
## 753 -0.300065783 5.00 -0.9365595883 4.1422896
## 754 -0.300065783 5.50 -1.2366253718 4.7115041
## 755 -0.300065783 6.00 -1.5366911553 2.8903962
## 756 0.194274549 4.75 -0.0448320258 3.1984035
## 757 0.194274549 5.25 0.1494425230 4.8210779
## 758 0.194274549 5.75 0.3437170718 5.1427432
## 759 0.194274549 6.25 0.5379916206 5.5431206
## 760 0.194274549 6.75 0.7322661694 8.0160893
## 761 0.101143620 4.00 0.3263464761 6.2428017
## 762 0.101143620 4.50 0.4274900958 4.7612152
## 763 0.101143620 5.00 0.5286337155 5.9189815
## 764 0.101143620 5.50 0.6297773352 4.5427794
## 765 0.101143620 6.00 0.7309209549 6.4458447
## 766 -0.739708409 4.75 0.2344688350 4.7013666
## 767 -0.739708409 5.25 -0.5052395742 3.9868796
## 768 -0.739708409 5.75 -1.2449479835 5.3231341
## 769 -0.739708409 6.25 -1.9846563928 4.0497534
## 770 -0.739708409 6.75 -2.7243648020 3.8096736
## 771 -0.567118109 4.00 0.1011030035 1.7916132
## 772 -0.567118109 4.50 -0.4660151052 3.4826272
## 773 -0.567118109 5.00 -1.0331332139 4.4696619
## 774 -0.567118109 5.50 -1.6002513227 4.9977745
## 775 -0.567118109 6.00 -2.1673694314 3.8737272
## 776 0.385314902 4.75 0.9748385857 5.6186259
## 777 0.385314902 5.25 1.3601534879 7.6613235
## 778 0.385314902 5.75 1.7454683901 8.5545173
## 779 0.385314902 6.25 2.1307832922 8.3457087
## 780 0.385314902 6.75 2.5160981944 8.3888229
## 781 0.326414691 4.00 0.1448335167 4.6832658
## 782 0.326414691 4.50 0.4712482079 5.4013417
## 783 0.326414691 5.00 0.7976628991 5.8479724
## 784 0.326414691 5.50 1.1240775902 7.8076716
## 785 0.326414691 6.00 1.4504922814 8.8965379
## 786 0.385946500 4.75 0.0784079149 5.4961266
## 787 0.385946500 5.25 0.4643544145 5.5879506
## 788 0.385946500 5.75 0.8503009141 5.9153386
## 789 0.385946500 6.25 1.2362474137 6.9341574
## 790 0.385946500 6.75 1.6221939133 9.6809431
## 791 0.315523979 4.00 0.4490426898 3.2838185
## 792 0.315523979 4.50 0.7645666692 6.1914210
## 793 0.315523979 5.00 1.0800906486 5.2663488
## 794 0.315523979 5.50 1.3956146279 6.4103059
## 795 0.315523979 6.00 1.7111386073 7.7820164
## 796 1.071699935 4.00 0.2297567809 5.0898015
## 797 1.071699935 4.50 1.3014567162 4.8561153
## 798 1.071699935 5.00 2.3731566515 8.2940017
## 799 1.071699935 5.50 3.4448565867 7.5006832
## 800 1.071699935 6.00 4.5165565220 10.5633625
## 801 0.033866517 4.00 -0.5628062336 3.1031352
## 802 0.033866517 4.50 -0.5289397168 3.3197730
## 803 0.033866517 5.00 -0.4950732001 6.1463382
## 804 0.033866517 5.50 -0.4612066833 5.8535915
## 805 0.033866517 6.00 -0.4273401665 5.4810168
## 806 0.504284444 4.75 0.6477489898 7.5957081
## 807 0.504284444 5.25 1.1520334340 7.8672272
## 808 0.504284444 5.75 1.6563178782 6.2746719
## 809 0.504284444 6.25 2.1606023224 9.3276015
## 810 0.504284444 6.75 2.6648867666 8.1775908
## 811 -0.189344761 4.75 -0.8600501395 3.1817161
## 812 -0.189344761 5.25 -1.0493949007 4.2985179
## 813 -0.189344761 5.75 -1.2387396620 4.8225678
## 814 -0.189344761 6.25 -1.4280844232 6.5931037
## 815 -0.189344761 6.75 -1.6174291844 5.2642324
## 816 0.053060720 4.75 0.6363263155 5.0321706
## 817 0.053060720 5.25 0.6893870355 6.2287589
## 818 0.053060720 5.75 0.7424477554 7.3609691
## 819 0.053060720 6.25 0.7955084754 7.6625907
## 820 0.053060720 6.75 0.8485691953 6.8408485
## 821 0.337952179 4.00 0.2847993578 4.8859174
## 822 0.337952179 4.50 0.6227515371 4.5740965
## 823 0.337952179 5.00 0.9607037164 7.0707996
## 824 0.337952179 5.50 1.2986558957 7.3633724
## 825 0.337952179 6.00 1.6366080750 8.6443128
## 826 0.945512653 4.75 -0.2264678216 3.4670749
## 827 0.945512653 5.25 0.7190448310 5.4899039
## 828 0.945512653 5.75 1.6645574836 6.8694183
## 829 0.945512653 6.25 2.6100701363 8.1581306
## 830 0.945512653 6.75 3.5555827889 9.4637864
## 831 -0.321083515 4.75 0.6954626702 3.9991362
## 832 -0.321083515 5.25 0.3743791553 6.0986495
## 833 -0.321083515 5.75 0.0532956403 5.9432909
## 834 -0.321083515 6.25 -0.2677878746 5.8222866
## 835 -0.321083515 6.75 -0.5888713895 6.0184953
## 836 0.908885771 4.00 -0.3773560800 4.7877041
## 837 0.908885771 4.50 0.5315296906 5.0187723
## 838 0.908885771 5.00 1.4404154613 5.9886306
## 839 0.908885771 5.50 2.3493012319 9.2998013
## 840 0.908885771 6.00 3.2581870026 11.1986325
## 841 0.451408328 4.75 0.5602374225 4.8404469
## 842 0.451408328 5.25 1.0116457503 6.9727130
## 843 0.451408328 5.75 1.4630540781 8.7482365
## 844 0.451408328 6.25 1.9144624058 9.4256541
## 845 0.451408328 6.75 2.3658707336 9.4841376
## 846 0.175349650 4.75 0.2103791336 5.1292299
## 847 0.175349650 5.25 0.3857287839 3.7877808
## 848 0.175349650 5.75 0.5610784343 7.8668156
## 849 0.175349650 6.25 0.7364280846 7.4732949
## 850 0.175349650 6.75 0.9117777350 7.5161941
## 851 -0.647280892 4.75 -0.2016022496 2.5125023
## 852 -0.647280892 5.25 -0.8488831411 5.3427586
## 853 -0.647280892 5.75 -1.4961640327 4.4236521
## 854 -0.647280892 6.25 -2.1434449242 3.8323759
## 855 -0.647280892 6.75 -2.7907258158 4.1026438
## 856 -0.311259349 4.75 -0.1009182101 4.5439311
## 857 -0.311259349 5.25 -0.4121775593 4.2292002
## 858 -0.311259349 5.75 -0.7234369085 5.7044099
## 859 -0.311259349 6.25 -1.0346962577 6.4601447
## 860 -0.311259349 6.75 -1.3459556069 4.2878434
## 861 -0.050669566 4.00 0.1927255524 4.7709742
## 862 -0.050669566 4.50 0.1420559861 4.3977556
## 863 -0.050669566 5.00 0.0913864198 4.3103125
## 864 -0.050669566 5.50 0.0407168535 5.7335791
## 865 -0.050669566 6.00 -0.0099527127 7.1294598
## 866 -0.233784797 4.75 0.5209444467 5.5101049
## 867 -0.233784797 5.25 0.2871596500 4.5608452
## 868 -0.233784797 5.75 0.0533748533 6.3271329
## 869 -0.233784797 6.25 -0.1804099434 6.7678134
## 870 -0.233784797 6.75 -0.4141947401 7.6572318
## 871 -1.196111326 4.00 0.5182508280 4.9268612
## 872 -1.196111326 4.50 -0.6778604982 6.1063015
## 873 -1.196111326 5.00 -1.8739718244 2.4294604
## 874 -1.196111326 5.50 -3.0700831505 2.4350572
## 875 -1.196111326 6.00 -4.2661944767 3.1161206
## 876 0.265343543 4.00 -0.5731007304 2.9439389
## 877 0.265343543 4.50 -0.3077571878 3.9709442
## 878 0.265343543 5.00 -0.0424136452 5.6986477
## 879 0.265343543 5.50 0.2229298974 5.8189447
## 880 0.265343543 6.00 0.4882734400 5.1153593
## 881 0.357486561 4.75 0.0958979056 4.5640333
## 882 0.357486561 5.25 0.4533844662 6.2357213
## 883 0.357486561 5.75 0.8108710267 6.8292489
## 884 0.357486561 6.25 1.1683575873 6.6148072
## 885 0.357486561 6.75 1.5258441478 9.0855399
## 886 0.007513131 4.75 0.1470837565 4.8732289
## 887 0.007513131 5.25 0.1545968874 5.5670523
## 888 0.007513131 5.75 0.1621100184 5.6149606
## 889 0.007513131 6.25 0.1696231493 6.1852575
## 890 0.007513131 6.75 0.1771362802 6.2681309
## 891 0.160394035 4.00 -0.0593148097 3.4494112
## 892 0.160394035 4.50 0.1010792254 6.1934569
## 893 0.160394035 5.00 0.2614732604 5.8375616
## 894 0.160394035 5.50 0.4218672954 5.4149077
## 895 0.160394035 6.00 0.5822613304 6.8385874
## 896 0.421312987 4.75 0.1138152130 4.1734393
## 897 0.421312987 5.25 0.5351281998 4.7552128
## 898 0.421312987 5.75 0.9564411866 6.8618332
## 899 0.421312987 6.25 1.3777541734 7.2395136
## 900 0.421312987 6.75 1.7990671602 7.9320666