qgraph (version 0.4.5)

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", ...)

Arguments

cor

A correlation matrix

factors

The number of factors to extract

rotation

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

...

arguments passed to qgraph.loadings

References

https://sites.google.com/site/qgraphproject 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

#### VISUALIZE CORRELATION MATRIX ###
eta=matrix(rnorm(200*5),ncol=5)
lam=matrix(0,nrow=100,ncol=5)
for (i in 1:5) lam[(20*i-19):(20*i),i]=rnorm(20,0.7,0.3)
eps=matrix(rnorm(200*100),ncol=100)
Y=eta%*%t(lam)+eps

# Run qgraph:
qgraph.pca(cor(Y),5,vsize=c(1,10))

# Show crossloadings:
qgraph.pca(cor(Y),5,crossloadings=T,cut=0,vsize=c(1,10))

# Wider plot with larger nodes:
Q=qgraph.pca(cor(Y),5,vsize=c(2,10),width=17)

# With measurement model:
groups=list(1:20,21:40,41:60,61:80,81:100)
names(groups)=LETTERS[1:5]

qgraph.pca(cor(Y),5,,Q,groups=groups,legend=FALSE)

# Circulair layout:
qgraph.pca(cor(Y),5,vsize=c(2,10),layout="circle",groups=groups,
	legend=FALSE)