Variables {mlmpower}

Functions for Creating Variables

Description

These functions are the building blocks used to create the multilevel model and are used to specify the names, properties, and variable types.

Usage

outcome(name, mean = 10, sd = 5, icc = NULL)

within_predictor(name, weight = 1, mean = 0, sd = 1, icc = NULL)

within_time_predictor(name, values, weight = 1)

between_predictor(name, weight = 1, mean = 0, sd = 1)

between_binary_predictor(name, proportion = 0.5, weight = 1)

Arguments

name

a character string for the specific variable's name

mean

a single numeric value that specifies the variable's mean

sd

a single numeric value that specifies the variable's standard deviation

icc

a single numeric value between 0 and 1 that specifies the variable's intraclass correlation. If NULL then the global ICC specified in effect_size() is used instead.

weight

a single numeric value specifying the variable's contribution to the variance explained metric. Weights are normalized across all variables of the same level.

values

a numeric vector specifying the time scores that will be repeated within each cluster.

proportion

a single numeric value between 0 and 1 that specifies the proportion of 1's at the population.

Details

Note that specifying an icc = 0 in within_predictor() will result in a centered within cluster (CWC) predictor.

See vignettes for more details.

vignette(package = 'mlmpower')

Value

Returns a mp_variable object based on the variable's type.