qgraph (version 1.5)

qgraph.pca: qgraph.pca

Description

This function performs an Principal Component Analysis (PCA) using the 'princomp' function of the psych package (Revelle, 2010) and sends the acquired factor loadings to qgraph.loadings.

Usage

qgraph.pca( cor, factors=1, ..., rotation="promax", factorCors = TRUE)

Arguments

cor

A correlation matrix or a "principal" object

factors

The number of factors to extract

...

arguments passed to qgraph.loadings

rotation

rotation to be used. Can be "varimax", "promax" or "none"

factorCors

Logical, should the correlations between factors be plotted? Defaults to TRUE

References

Sacha Epskamp, Angelique O. J. Cramer, Lourens J. Waldorp, Verena D. Schmittmann, Denny Borsboom (2012). qgraph: Network Visualizations of Relationships in Psychometric Data. Journal of Statistical Software, 48(4), 1-18. URL http://www.jstatsoft.org/v48/i04/.

Revelle, W. (2010) psych: Procedures for Personality and Psychological Research Northwestern University, Evanston, http://personality-project.org/r/psych.manual.pdf, 1.0-93

See Also

qgraph qgraph.efa qgraph.loadings

Examples

# NOT RUN {
data(big5)
data(big5groups)

qgraph.pca(cor(big5),5,groups=big5groups,rotation="promax",minimum=0.2,
			cut=0.4,vsize=c(1,7),borders=FALSE,vTrans=200)

# Tree layout:
qgraph.pca(cor(big5), 5,groups=big5groups, rotation="promax", minimum=0.2, 
  cut=0.4, vsize=c(1.5,7), borders=FALSE, layout="tree", width=20, filetype="R")
# }