fa.sort function

Sort factor analysis or principal components analysis loadings

Although the print.psych function will sort factor analysis loadings, sometimes it is useful to do this outside of the print function. fa.sort takes the output from the fa or principal functions and sorts the loadings for each factor. Items are located in terms of their greatest loading. The new order is returned as an element in the fa list. fa.organize allows for the columns or rows to be reorganized. 1.1

fa.sort(fa.results,polar=FALSE)
fa.organize(fa.results,o=NULL,i=NULL,cn=NULL,echelon=TRUE,flip=TRUE)

Arguments

• fa.results: The output from a factor analysis or principal components analysis using fa or principal. Can also just be a matrix of loadings. Can also organize the output of cluster loadings from iclust.
• polar: Sort by polar coordinates of first two factors (FALSE)
• o: The order in which to order the factors
• i: The order in which to order the items
• cn: new factor names
• echelon: Organize the factors so that they are in echelon form (variable 1 .. n on factor 1, n+1 ...n=k on factor 2, etc.)
• flip: Flip factor loadings such that the colMean is positive.

Details

The fa.results$loadings are replaced with sorted loadings.

fa.organize takes a factor analysis or components output and reorganizes the factors in the o order. Items are organized in the i order. This is useful when comparing alternative factor solutions.

The flip option works only for the case of matrix input, not for full fa objects. Use the reflect function.

Returns

A sorted factor analysis, principal components analysis, or omega loadings matrix.

These sorted values are used internally by the various diagram functions.

The values returned are the same as fa, except in sorted order. In addition, the order is returned as an additional element in the fa list.

Author(s)

William Revelle

See Also

See Also as fa, pca, iclust, print.psych, fa.diagram,

Examples

test.simple <- fa(sim.item(16),2)
fa.sort(test.simple)
fa.organize(test.simple,c(2,1))  #the factors but not the items have been rearranged