psychmeta (version 2.3.4)

simulate_psych: Simulate Monte Carlo psychometric data (observed, true, and error scores)

Description

Simulate Monte Carlo psychometric data (observed, true, and error scores)

Usage

simulate_psych(n, rho_mat, mu_vec = rep(0, ncol(rho_mat)),
  sigma_vec = rep(1, ncol(rho_mat)), rel_vec = rep(1, ncol(rho_mat)),
  sr_vec = rep(1, ncol(rho_mat)), k_items_vec = rep(1, ncol(rho_mat)),
  wt_mat = NULL, sr_composites = NULL, var_names = NULL,
  composite_names = NULL)

Arguments

n

Number of cases to simulate before performing selection.

rho_mat

Matrix of true-score correlations.

mu_vec

Vector of means.

sigma_vec

Vector of observed-score standard deviations.

rel_vec

Vector of reliabilities corresponding to the variables in rho_mat.

sr_vec

Vector of selection ratios corresponding to the variables in rho_mat. (set selection ratios to 1 for variables that should not be used in selection).

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.

var_names

Vector of variable names corresponding to the variables in rho_mat.

composite_names

Optional vector of names for composite variables.

...

Further arguments.

Value

A list of observed-score, true-score, and error-score data frames. If selection is requested, the data frames will include logical variables indicating whether each case would be selected on the basis of observed scores, true scores, or error scores.

Examples

# NOT RUN {
## Generate data for a simple sample with two variables without selection:
simulate_psych(n = 1000, rho_mat = matrix(c(1, .5, .5, 1), 2, 2), sigma_vec = c(1, 1),
          rel_vec = c(.8, .8), var_names = c("Y", "X"))

## Generate data for a simple sample with two variables with selection:
simulate_psych(n = 1000, rho_mat = matrix(c(1, .5, .5, 1), 2, 2), sigma_vec = c(1, 1),
          rel_vec = c(.8, .8), sr_vec = c(1, .5), var_names = c("Y", "X"))

## Generate data for samples with five variables, of which subsets are used to form composites:
rho_mat <- matrix(.5, 5, 5)
diag(rho_mat) <- 1
simulate_psych(n = 1000, rho_mat = rho_mat,
                rel_vec = rep(.8, 5), sr_vec = c(1, 1, 1, 1, .5),
                wt_mat = cbind(c(0, 0, 0, .3, 1), c(1, .3, 0, 0, 0)), sr_composites = c(.7, .5))

## Generate data for similar scenario as above, but with scales consisting of 1-5 items:
rho_mat <- matrix(.5, 5, 5)
diag(rho_mat) <- 1
simulate_psych(n = 1000, rho_mat = rho_mat,
                rel_vec = rep(.8, 5), sr_vec = c(1, 1, 1, 1, .5),
                k_items_vec = 1:5,
                wt_mat = cbind(c(0, 0, 0, .3, 1), c(1, .3, 0, 0, 0)), sr_composites = c(.7, .5))
# }