simpca: Simulate Data with a Specific Principal Components Structure...

In cds: Constrained Dual Scaling for Detecting Response Styles

Simulate Data with a Specific Principal Components Structure and Response Style Contamination

Description

Simulate normally distributed data with specific covariance structure and randomly sampled means. Adds response style contamination.

Usage

simpca(
  nr.indv = rep(200, 5),
  m = 10,
  q = 7,
  R = rcormat(m = m),
  err.coeff = 0.1,
  alphamat = rbind(c(0.5, 2, 4), c(10, 2, 10), c(1, 2, 1), c(4, 2, 0.5), c(0.1, 2,
    0.1))[1:length(nr.indv), ],
  randomize = FALSE
)

Arguments

nr.indv	Numeric vector of group sizes.
m	Integer; then number of variables to simulate.
q	Integer; the rating scale used 1:q.
R	List with entry named 'R' which is the simulated correlation matrix
err.coeff	Standard error for each variable, added unto R.
alphamat	Matrix containing splines coefficients for te construction of respone styles.
randomize	logical; should the rows of the data be randomly permuted or not?

cds documentation built on April 16, 2025, 1:10 a.m.