generate {mlmpower}

Generates Data Sets Based on a mp_model

Description

Generates data sets based on a mp_model. These data sets will be returned as a data.frame and include the solved parameters as an attribute to the data.frame.

Usage

generate(model, n_within, n_between, ndata = 1, mechanism = NULL)

Arguments

model

a mp_model.

n_within

a single positive integer of the desired within cluster observations.

n_between

a single positive integer of the desired between cluster observations.

ndata

a single positive integer of the number of desired data sets.

mechanism

a function for inducing missing data to the data set. If NULL it is ignored. See details below.

Details

Note that there must only be one global ICC in mp_model.

Use the mechanism argument to specify missing data mechanisms. See mechanisms for predefined missing data mechanisms for the outcome and examples using them. When creating custom mechanisms care needs to be taken because it is considered for advanced usage. This a argument expects a function with the mp_data as the input, and the function should return the modified mp_data. Be careful when using this because it allows you to modify the population parameters, which will be incorrect. You should only induce missing data values on variables. Missing data on the predictors will cause listwise deletion to be used, but missing data on the outcome will be appropriate for MAR-based mechanisms. See examples below for an example that generates MCAR data on the outcome. See parameters to obtain the population parameters from each data set.

Value

For ndata = 1 a single data.frame is returned. The first variable is the cluster identifier labeled ⁠_id⁠. This object is also of class mp_data which means that it was generated based on a specified model. If multiple data sets are requested then they will be contained in a list.

Examples

# Create Model
model <- (
    outcome('Y')
    + within_predictor('X')
    + effect_size(icc = 0.1)
)
# Set seed
set.seed(198723)
# Create data set
model |> generate(5, 50) -> mydata

# Induce missing data with custom function
model |> generate(50, 5, mechanism = MCAR(0.25)) -> mydata_mcar

# Induce missing data with custom function
model |> generate(50, 5, mechanism = \(data) {
    # `data` will be the `mp_data` used
    within(data, {
        # MCAR Process
        Y <- ifelse(runif(NROW(data)) < 0.5, Y, NA)
    })
}) -> mydata_mcar_custom