print.psych: Print and summary functions for the psych class

Description

Give limited output (print) or somewhat more detailed (summary) for most of the functions in psych.

Usage

print.psych(x,digits=2,all=FALSE,cut=NULL,sort=FALSE,...)
summary.psych(object,digits=2,items=FALSE,...)

Arguments

Output from a psych function (e.g., factor.pa, omega,ICLUST, score.items, cluster.cor

object

Output from a psych function

items

items=TRUE (default) does not print the item whole correlations

digits

Number of digits to use in printing

all

if all=TRUE, then the object is declassed and all output from the function is printed

cut

Cluster loadings < cut will not be printed. For the factor analysis functions (fa and factor.pa etc.), cut defaults to 0, for ICLUST to .3, for omega to .2.

sort

Cluster loadings are in sorted order

...

More options to pass to summary and print

Value

Various psych functions produce copious output. This is a way to summarize the most important parts of the output of the score.items, cluster.scores, and ICLUST functions. See those ( score.items, cluster.cor, cluster.loadings, or ICLUST) for details on what is produced.

Details

Most of the psych functions produce too much output. print.psych and summary.psych use generic methods for printing just the highlights. To see what else is available, ask for the structure of the particular object: (str(theobject).

Alternatively, to get complete output, unclass(theobject) and then print it.

As an added feature, if the promax function is applied to a factanal loadings matrix, the normal output just provides the rotation matrix. print.psych will provide the factor correlations. (Following a suggestion by John Fox and Uli Keller to the R-help list). The alternative is to just use the Promax function directly on the factanal object.

Examples

Run this code

data(bfi)
 keys.list <- list(agree=c(-1,2:5),conscientious=c(6:8,-9,-10),extraversion=c(-11,-12,13:15),neuroticism=c(16:20),openness = c(21,-22,23,24,-25))
 keys <- make.keys(25,keys.list,item.labels=colnames(bfi[1:25]))
 scores <- score.items(keys,bfi[1:25])
 scores
 summary(scores)

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