fa.graph: Graph factor loading matrices

Description

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).

Usage

fa.diagram(fa.results,sort=TRUE,labels=NULL,cut=.3,simple=TRUE,errors=FALSE,
    digits=1,e.size=.05,rsize=.15,side=2,main="Factor Analysis", ...) 
fa.graph(fa.results,out.file=NULL,labels=NULL,cut=.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",graphviz=TRUE, ...)

Arguments

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 (fa.graph)

labels

Variable labels

cut

Loadings with abs(loading) > cut will be shown

simple

Only the biggest loading per item is shown

size

graph size

sort

sort the factor loadings before showing the diagram

errors

include error estimates (as arrows)

e.size

size of ellipses

rsize

size of rectangles

side

on which side should error arrows go?

node.font

edge.font

rank.direction

digits

Number of digits to show as an edgelable

main

Graphic title

graphviz

Should we try to use Rgraphviz for output?

...

other parameters

Value

fa.diagram: A path diagram is drawn without using Rgraphviz. This is probably the more useful function.
fa.graph: A graph is drawn using rgraphviz. If an output file is specified, the graph instructions are also saved in the dot language.

Details

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.

fa.diagram does not use Rgraphviz and is the preferred function.

In fa.graph, 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.

Examples

Run this code

test.simple <- fa(item.sim(16),2,rotate="oblimin")
if(require(Rgraphviz)) {fa.graph(test.simple) }
fa.diagram(test.simple)
data(bifactor)
fa.diagram(fa(Thurstone,3,rotate="cluster"),cut=.4,digits=2)