Description Usage Arguments Details See Also Examples
Takes simulation parameters as inputs and returns simulated data.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  sim_reg_nested3(
fixed,
random,
random3,
fixed_param,
random_param = list(),
random_param3 = list(),
cov_param,
k,
n,
p,
error_var,
with_err_gen,
arima = FALSE,
data_str,
cor_vars = NULL,
fact_vars = list(NULL),
unbal = list(level2 = FALSE, level3 = FALSE),
unbal_design = list(level2 = NULL, level3 = NULL),
lvl1_err_params = NULL,
arima_mod = list(NULL),
contrasts = NULL,
homogeneity = TRUE,
heterogeneity_var = NULL,
cross_class_params = NULL,
knot_args = list(NULL),
...
)

fixed 
One sided formula for fixed effects in the simulation. To suppress intercept add 1 to formula. 
random 
One sided formula for random effects in the simulation. Must be a subset of fixed. 
random3 
One sided formula for random effects at third level in the simulation. Must be a subset of fixed (and likely of random). 
fixed_param 
Fixed effect parameter values (i.e. beta weights). Must be same length as fixed. 
random_param 
A list of named elements that must contain:
Optional elements are:

random_param3 
A list of named elements that must contain:
Optional elements are:

cov_param 
List of arguments to pass to the continuous generating function, must be the same order as the variables specified in fixed. This list does not include intercept, time, factors, or interactions. Required arguments include:
Optional arguments to the distribution functions are in a nested list, see the examples or vignettes for example code. 
k 
Number of third level clusters. 
n 
Level two cluster sample size within each level three cluster. 
p 
Within cluster sample size within each level two cluster. 
error_var 
Scalar of error variance. 
with_err_gen 
Simulated within cluster error distribution. Must be a quoted 'r' distribution function. 
arima 
TRUE/FALSE flag indicating whether residuals should
be correlated. If TRUE, must specify a valid model to pass to
arima.sim via the arima_mod argument.
See 
data_str 
Type of data. Must be "cross" or "long". 
cor_vars 
A vector of correlations between variables. 
fact_vars 
A nested list of factor, categorical, or ordinal variable specification, each list must include:
Optional arguments include:
See also 
unbal 
A named TRUE/FALSE list specifying whether unbalanced simulation design is desired. The named elements must be: "level2" or "level3" representing unbalanced simulation for level two and three respectively. Default is FALSE, indicating balanced sample sizes at both levels. 
unbal_design 
When unbal = TRUE, this specifies the design for unbalanced simulation in one of two ways. It can represent the minimum and maximum sample size within a cluster via a named list. This will be drawn from a random uniform distribution with min and max specified. Secondly, the actual sample sizes within each cluster can be specified. This takes the form of a vector that must have the same length as the level two or three sample size. These are specified as a named list in which level two sample size is controlled via "level2" and level three sample size is controlled via "level3". 
lvl1_err_params 
Additional parameters passed as a list on to the level one error generating function 
arima_mod 
A list indicating the ARIMA model to pass to arima.sim.
See 
contrasts 
An optional list that specifies the contrasts to be used
for factor variables (i.e. those variables with .f or .c).
See 
homogeneity 
Either TRUE (default) indicating homogeneity of variance assumption is assumed or FALSE to indicate desire to generate heterogeneity of variance. 
heterogeneity_var 
Variable name as a character string to use for heterogeneity of variance simulation. 
cross_class_params 
A list of named parameters when cross classified data structures are desired. Must include the following arguments:

knot_args 
A nested list of named knot arguments. See

... 
Not currently used. 
Simulates data for the linear mixed model, both cross sectional and longitudinal data. Returns a data frame with ID variables, fixed effects, and many other variables useful to help when running simulation studies.
sim_reg
for a convenient wrapper for all data
conditions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  #' # Three level example
fixed < ~1 + time + diff + act + actClust + time:act
random < ~1 + time + diff
random3 < ~ 1 + time
fixed_param < c(4, 2, 6, 2.3, 7, 0)
random_param < list(random_var = c(7, 4, 2), rand_gen = 'rnorm')
random_param3 < list(random_var = c(4, 2), rand_gen = 'rnorm')
cov_param < list(dist_fun = c('rnorm', 'rnorm', 'rnorm'),
var_type = c("level1", "level2", "level3"),
opts = list(list(mean = 0, sd = 1.5),
list(mean = 0, sd = 4),
list(mean = 0, sd = 2)))
k < 10
n < 15
p < 10
error_var < 4
with_err_gen < 'rnorm'
data_str < "long"
temp_three < sim_reg(fixed, random, random3, fixed_param, random_param,
random_param3, cov_param, k,n, p, error_var, with_err_gen,
data_str = data_str)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.