fa.graph {psych} | R Documentation |
Factor analysis or principal components analysis results are typically interpreted in terms of the major loadings on each factor. These structures may be represented as a table of loadings or graphically, where all loadings with an absolute value > some cut point are represented as an edge (path).
fa.graph(fa.results, out.file = NULL, labels = NULL, cut = 0.3, simple = TRUE, size = c(8, 6), node.font = c("Helvetica", 14), edge.font = c("Helvetica", 10), rank.direction=c("RL","TB","LR","BT"), digits = 1, main = "Factor Analysis", ...)
fa.results |
The output of factor analysis or principal components analysis |
out.file |
If it exists, a dot representation of the graph will be stored here |
labels |
Variable labels |
cut |
Loadings with abs(loading) > cut will be shown |
simple |
Only the biggest loading per item is shown |
size |
graph size |
node.font |
|
edge.font |
|
rank.direction |
|
digits |
Number of digits to show as an edgelable |
main |
Graphic title |
... |
other parameters |
Path diagram representations have become standard in confirmatory factor analysis, but are not yet common in exploratory factor analysis. Representing factor structures graphically helps some people understand the structure.
Although a nice graph is drawn for the orthogonal factor case, the oblique factor drawing is acceptable, but is better if cleaned up outside of R.
A graph is drawn using rgraphviz. If an output file is specified, the graph instructions are also saved in the dot language.
Requires Rgraphviz. For the Mac, there are occasional difficulties installing Rgraphviz from Bioconductor in that some libraries are misplaced and need to be relinked using X11.
As of June 1, 2007 there is an occasionally strange result when using the simple=FALSE option in Sweave.
William Revelle
test.simple <- factor.pa(item.sim(16),2) if(require(Rgraphviz)) {fa.graph(test.simple) }