psych (version 1.5.1)

fa.diagram: 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). fa.diagram uses the various diagram functions to draw the diagram. fa.graph generates dot code for external plotting. fa.rgraph uses the Rgraphviz package (if available) to draw the graph. het.diagram will draw "heterarchy" diagrams of factor/scale solutions at different levels.

Usage

fa.diagram(fa.results,Phi=NULL,fe.results=NULL,sort=TRUE,labels=NULL,cut=.3,
     simple=TRUE, errors=FALSE,g=FALSE,digits=1,e.size=.05,rsize=.15,side=2,
    main,cex=NULL,marg=c(.5,.5,1,.5),adj=1, ...) 
het.diagram(r,levels,cut=.3,digits=2,both=TRUE, 
        main="Heterarchy diagram",...) 
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", ...)
fa.rgraph(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, principal components analysis, or ICLUST analysis. May also be a factor loading matrix from anywhere.

Phi

Normally not specified (it is is found in the FA, pc, or ICLUST, solution), this may be given if the input is a loadings matrix.

fe.results

the results of a factor extension analysis (if any)

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

g

Does the factor matrix reflect a g (first) factor. If so, then draw this to the left of the variables, with the remaining factors to the right of the variables. It is useful to turn off the simple parameter in this case.

r

A correlation matrix for the het.diagram function

levels

A list of the elements in each level

both

Should arrows have double heads (in het.diagram)

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?

cex

modify font size

marg

sets the margins to be wider than normal, returns them to the normal size upon exit

adj

how many different positions (1-3) should be used for the numeric labels. Useful if they overlap each other.

node.font

what font should be used for nodes in fa.graph

edge.font

what font should be used for edges in fa.graph

rank.direction

parameter passed to Rgraphviz-- which way to draw the graph

digits

Number of digits to show as an edgelable

main

Graphic title, defaults to "factor analyis" or "factor analysis and extension"

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.rgraph: A graph is drawn using rgraphviz. If an output file is specified, the graph instructions are also saved in the dot language.

  fa.graph: the graph instructions are 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. fa.graph generates dot code to be used by an external graphics program. It does not have all the bells and whistles of fa.diagram, but these may be done in the external editor.

Hierarchical (bifactor) models may be drawn by specifying the g parameter as TRUE. This allows for an graphical displays of various factor transformations with a bifactor structure (e.g., bifactor and biquartimin. See omega for an alternative way to find these structures.

In fa.rgraph 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 or done using fa.diagram.

The normal input is taken from the output of either fa or ICLUST. This latter case displays the ICLUST results in terms of the cluster loadings, not in terms of the cluster structure. Actually an interesting option.

It is also possible to just give a factor loading matrix as input. In this case, supplying a Phi matrix of factor correlations is also possible.

It is possible, using fa.graph, to export dot code for an omega solution. fa.graph should be applied to the schmid$sl object with labels specified as the rownames of schmid$sl. The results will need editing to make fully compatible with dot language plotting.

To specify the model for a structural equation confirmatory analysis of the results, use structure.diagram instead.

See Also

omega.graph, ICLUST.graph, structure.diagram to convert the factor diagram to sem modeling code.

Examples

test.simple <- fa(item.sim(16),2,rotate="oblimin")
#if(require(Rgraphviz)) {fa.graph(test.simple) } 
fa.diagram(test.simple)
f3 <- fa(Thurstone,3,rotate="cluster")
fa.diagram(f3,cut=.4,digits=2)
f3l <- f3$loadings
fa.diagram(f3l,main="input from a matrix")
Phi <- f3$Phi
fa.diagram(f3l,Phi=Phi,main="Input from a matrix")
fa.diagram(ICLUST(Thurstone,2,title="Two cluster solution of Thurstone"),main="Input from ICLUST")