psychmeta (version 2.3.4)

simulate_d_sample: Simulate a sample of psychometric d value data with measurement error, direct range restriction, and/or indirect range restriction

Description

This function generates a simulated psychometric sample consisting of any number of groups and computes the d values that result after introducing measurement error and/or range restriction.

Usage

simulate_d_sample(n_vec, rho_mat_list, mu_mat, sigma_mat = 1,
  rel_mat = 1, sr_vec = 1, k_items_vec = 1, wt_mat = NULL,
  sr_composites = NULL, group_names = NULL, var_names = NULL,
  composite_names = NULL, diffs_as_obs = FALSE)

Arguments

n_vec

Vector of sample sizes (or a vector of proportions, if parameters are to be estimated).

rho_mat_list

List of true-score correlation matrices.

mu_mat

Matrix of mean parameters, with groups on the rows and variables on the columns.

sigma_mat

Matrix of standard-deviation parameters, with groups on the rows and variables on the columns.

rel_mat

Matrix of reliability parameters, with groups on the rows and variables on the columns.

sr_vec

Vector of selection ratios.

k_items_vec

Number of test items comprising each of the variables to be simulated (all are single-item variables by default).

wt_mat

Optional matrix of weights to use in forming a composite of the variables in rho_mat. Matrix should have as many rows (or vector elements) as there are variables in rho_mat.

sr_composites

Optional vector selection ratios for composite variables. If not NULL, sr_composites must have as many elements as there are columns in wt_mat.

group_names

Optional vector of group names.

var_names

Optional vector of variable names.

composite_names

Optional vector of names for composite variables.

diffs_as_obs

Logical scalar that determines whether standard deviation parameters represent standard deviations of observed scores (TRUE) or of true scores (FALSE; default).

Value

A sample of simulated mean differences.

Examples

# NOT RUN {
## Simulate statistics by providing integers as "n_vec":
simulate_d_sample(n_vec = c(200, 100), rho_mat_list = list(reshape_vec2mat(.5),
                                                           reshape_vec2mat(.4)),
                  mu_mat = rbind(c(1, .5), c(0, 0)), sigma_mat = rbind(c(1, 1), c(1, 1)),
                  rel_mat = rbind(c(.8, .7), c(.7, .7)), sr_vec = c(1, .5),
                  group_names = c("A", "B"))

## Simulate parameters by providing proportions as "n_vec":
simulate_d_sample(n_vec = c(2/3, 1/3), rho_mat_list = list(reshape_vec2mat(.5),
                                                           reshape_vec2mat(.4)),
                  mu_mat = rbind(c(1, .5), c(0, 0)), sigma_mat = rbind(c(1, 1), c(1, 1)),
                  rel_mat = rbind(c(.8, .7), c(.7, .7)), sr_vec = c(1, .5),
                  group_names = c("A", "B"))
# }