biplot.psych {psych} | R Documentation

## Draw biplots of factor or component scores by factor or component loadings

### Description

Extends the biplot function to the output of `fa`, `fa.poly` or `principal`. Will plot factor scores and factor loadings in the same graph. If the number of factors > 2, then all pairs of factors are plotted. Factor score histograms are plotted on the diagonal. The input is the resulting object from `fa`, `principal`, or }code{linkfa.poly with the scores=TRUE option. Points may be colored according to other criteria.

### Usage

    ## S3 method for class 'psych'
    biplot(x, labels=NULL,cex=c(.75,1),main="Biplot from fa",
    hist.col="cyan",xlim.s=c(-3,3),ylim.s=c(-3,3),xlim.f=c(-1,1),ylim.f=c(-1,1),
    maxpoints=100,adjust=1.2,col,pos, arrow.len = 0.1,pch=16,choose=NULL,
    cuts=1,cutl=.0,group=NULL,...)

### Arguments

`x` | The output from `fa`, `fa.poly` or `principal` with the scores=TRUE option
`labels` | if NULL, draw the points with the plot character (pch) specified. To identify the data points, specify labels= 1:n where n is the number of observations, or labels =rownames(data) where data was the data set analyzed by the factor analysis.
`cex` | A vector of plot sizes of the data labels and of the factor labels
`main` | A main title for a two factor biplot
`hist.col` | If plotting more than two factors, the color of the histogram of the factor scores
`xlim.s` | x limits of the scores. Defaults to plus/minus three sigma
`ylim.s` | y limits of the scores.Defaults to plus/minus three sigma
`xlim.f` | x limits of the factor loadings.Defaults to plus/minus 1.0
`ylim.f` | y limits of the factor loadings.Defaults to plus/minus 1.0
`maxpoints` | When plotting 3 (or more) dimensions, at what size should we switch from plotting "o" to plotting "."
`adjust` | an adjustment factor in the histogram
`col` | a vector of colors for the data points and for the factor loading labels
`pos` | If plotting labels, what position should they be in? 1=below, 2=left, 3 top, 4 right. If missing, then the assumption is that labels should be printed instead of data points.
`arrow.len` | the length of the arrow head
`pch` | The plotting character to use. pch=16 gives reasonable size dots. pch="." gives tiny points. If adding colors, use pch between 21 and 25. (see examples).
`choose` | Plot just the specified factors
`cuts` | Do not label cases with abs(factor scores) < cuts) (Actually, the distance of the x and y scores from 0)
`cutl` | Do not label variables with communalities in the two space < cutl
`group` | A vector of a grouping variable for the scores. Show a different color and symbol for each group.
`...` | more options for graphics

### Details

Uses the generic biplot function to take the output of a factor analysis `fa`, `fa.poly` or principal components analysis `principal` and plot the factor/component scores along with the factor/component loadings.

This is an extension of the generic biplot function to allow more control over plotting points in a two space and also to plot three or more factors (two at time).

This will work for objects produced by `fa`, `fa.poly` or `principal` if they applied to the original data matrix. If however, one has a correlation matrix based upon the output from `tetrachoric` or `polychoric`, and has done either `fa` or `principal` on the correlations, then obviously, we can not do a biplot. However, both of those functions produce a weights matrix, which, in combination with the original data can be used to find the scores by using `factor.scores`. Since biplot.psych is looking for two elements of the x object: x$loadings and x$scores, you can create the appropriate object to plot. See the third example.

### Author(s)

William Revelle

### See Also

`fa`, `fa.poly`, `principal`, `fa.plot`, `pairs.panels`

### Examples

    #the standard example
    data(USArrests)
    fa2 <- fa(USArrests,2,scores=TRUE)
    biplot(fa2,labels=rownames(USArrests))

    # plot the 3 factor solution
    data(bfi)
    fa3 <- fa(bfi[1:200,1:15],3,scores=TRUE)
    biplot(fa3)
    #just plot factors 1 and 3 from that solution
    biplot(fa3,choose=c(1,3))

    #
    fa2 <- fa(bfi[16:25],2)  #factor analysis
    fa2$scores <- fa2$scores[1:100,]  #just take the first 100
    #now plot with different colors and shapes for males and females
    biplot(fa2,pch=c(24,21)[bfi[1:100,"gender"]],group =bfi[1:100,"gender"],
       main="Biplot of Conscientiousness and Neuroticism by gender")

    r <- cor(bfi[1:200,1:10], use="pairwise") #find the correlations
    f2 <- fa(r,2)
    x <- list()
    x$scores <- factor.scores(bfi[1:200,1:10],f2)
    x$loadings <- f2$loadings
    class(x) <- c('psych','fa')
    biplot(x,main="biplot from correlation matrix and factor scores")

[Package psych version 1.6.9 Index]