qgraph.pca {qgraph} | R Documentation |
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
.
qgraph.pca( cor, factors=1, ..., rotation="promax", factorCors = TRUE)
cor |
A correlation matrix or a "principal" object |
factors |
The number of factors to extract |
... |
arguments passed to |
rotation |
rotation to be used. Can be "varimax", "promax" or "none" |
factorCors |
Logical, should the correlations between factors be plotted? Defaults to TRUE |
Sacha Epskamp (s.epskamp@uva.nl)
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
qgraph
qgraph.efa
qgraph.loadings
data(big5) data(big5groups) qgraph.pca(cor(big5),5,groups=big5groups,rotation="promax",minimum=0.2, cut=0.4,vsize=c(1,15),borders=FALSE,asize=0.07,esize=4,vTrans=200) # Tree layout: qgraph.pca(cor(big5), 5,groups=big5groups, rotation="promax", minimum=0.2, cut=0.4, vsize=c(1,15), borders=FALSE, asize=0.07, esize=4, layout="tree", width=20, filetype="R")