gen_data {paramtest}

Generate data through a factor matrix and effects matrix.

Description

gen_data will generate sample data based on a factor structure and effects structure specified by the user.

Usage

gen_data(factor_struct, effects_struct, n_cases = 1000, true_scores = FALSE)

Arguments

factor_struct

A matrix describing the measurement model of latent factors (columns) as measured by observed variables (rows).

effects_struct

A matrix describing the variances and covariances of the latent variables in the model.

n_cases

Number of sample cases to generate.

true_scores

Whether or not to include the data for each variable as measured without error. If set to TRUE, the resulting data frame will include all the variables in the model twice: once with measurement error, and once without.

Value

Returns a data frame with n_cases rows and columns for each observed and latent variable. These variables will approximately accord with the factor structure and effects structure that was specified, within sampling error.

Examples

# two uncorrelated predictors, one criterion, with measurement error in all
# variables
beta1 <- .5
beta2 <- .6
y_resid_var <- sqrt(1 - (beta1^2 + beta2^2))
fmodel <- matrix(
    c(.8, 0, 0,   # x1
      0, .6, 0,   # x2
      0, 0, .5),  # y
    nrow=3, ncol=3, byrow=TRUE, dimnames=list(
    c('x1', 'x2', 'y'), c('x1', 'x2', 'y')))
    # in this case, observed and latent variables are the same
effects <- matrix(
    c(1, 0, beta1,
      0, 1, beta2,
      0, 0, y_resid_var),
    nrow=3, ncol=3, byrow=TRUE, dimnames=list(
    c('x1', 'x2', 'y'), c('x1', 'x2', 'y')))

sample_data <- gen_data(fmodel, effects, n_cases=1000)
round(var(sample_data), 2)
round(cor(sample_data), 2)
summary(lm(y ~ x1 + x2, data=sample_data))
    # note that beta coefficients are much smaller, due to measurement error