factor.fit: How well does the factor model fit a correlation matrix. Part...

In frenchja/psych: Procedures for Psychological, Psychometric, and Personality Research

Description

The basic factor or principal components model is that a correlation or covariance matrix may be reproduced by the product of a factor loading matrix times its transpose: F'F or P'P. One simple index of fit is the 1 - sum squared residuals/sum squared original correlations. This fit index is used by VSS, ICLUST, etc.

Usage

factor.fit(r, f)

Arguments

r	a correlation matrix
f	A factor matrix of loadings.

Details

There are probably as many fit indices as there are psychometricians. This fit is a plausible estimate of the amount of reduction in a correlation matrix given a factor model. Note that it is sensitive to the size of the original correlations. That is, if the residuals are small but the original correlations are small, that is a bad fit.

Let

R*= R - FF'

fit = 1 - sum(R*^2)/sum(R^2)

.

The sums are taken for the off diagonal elements.

Value

fit

Author(s)

William Revelle

See Also

VSS, ICLUST

Examples

## Not run: 
#compare the fit of 4 to 3 factors for the Harman 24 variables
fa4 <- factanal(x,4,covmat=Harman74.cor$cov)
round(factor.fit(Harman74.cor$cov,fa4$loading),2)
#[1] 0.9
fa3 <- factanal(x,3,covmat=Harman74.cor$cov)
round(factor.fit(Harman74.cor$cov,fa3$loading),2)
#[1] 0.88


## End(Not run)

frenchja/psych documentation built on May 16, 2019, 2:49 p.m.