genPCA: Generate PCA data and Calculates Correlation Matrices

In cds: Constrained Dual Scaling for Detecting Response Styles

Description

Generate a response style data set from a specific correlation matrix, clean the data with constrained dual scaling and report the original, cleaned and contaminated correlation matrices in a list.

Usage

genPCA(nr.indv = rep(100, 5), m = 10, q = 7, r = 3, 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 = TRUE, ...)

Arguments

nr.indv	Vector; number of individuals in each response style group. It is passed to simpca.
m	scalar; Number of items.
q	scalar; Number of rating categories, such that the rating scale is 1:q.
r	scalar; Rank of simulated correlation matrices.
err.coeff	scalar; Standard deviation used in simulations that is passed on to simpca.
alphamat	matrix; Contains the spline parameters for the different response styles that is passed to simpca.
randomize	logical; See simpca.
...	Arguments passed to cds.

Value

A list with components:

Rsim	Correlation matrix from which the sample was generated
Rclean	Correlation matrix for the cleaned data
Rcont	Correlation matrix for the contaminated data

Author(s)

Pieter C. Schoonees

cds documentation built on May 2, 2019, 5:54 a.m.