summary.nScree {nFactors}

Utility Functions for nScree Class Objects

Description

Utility functions for nScree class objects. Some of these functions are already implemented in the nFactors package, but are easier to use with generic functions like these.

Usage

## S3 method for class 'nScree'
summary(object, ...)

## S3 method for class 'nScree'
print(x, ...)

## S3 method for class 'nScree'
plot(x, ...)

is.nScree(object)

Arguments

object

nScree: an object of the class nScree

...

variable: additionnal parameters to give to the print function with print.nScree, the plotnScree with plot.nScree or to the summary function with summary.nScree

x

Results of a previous nScree analysis

Value

Generic functions for the nScree class:

is.nScree

logical: is the object of the class nScree?

plot.nScree

graphic: plots a figure according to the plotnScree function

print.nScree

numeric: vector of the number of components/factors to retain: same as the Components vector from the nScree object

summary.nScree

data.frame: details of the results from a nScree analysis: same as the Analysis data.frame from the nScree object, but with easier control of the number of decimals with the digits parameter

Author(s)

Gilles Raiche
Centre sur les Applications des Modeles de Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
[e-mail]

References

Raiche, G., Walls, T. A., Magis, D., Riopel, M. and Blais, J.-G. (2013). Non-graphical solutions for Cattell's scree test. Methodology, 9(1), 23-29.

Examples

## INITIALISATION
 data(dFactors)                      # Load the nFactors dataset
 attach(dFactors)
 vect         <- Raiche              # Use the example from Raiche
 eigenvalues  <- vect$eigenvalues    # Extract the observed eigenvalues
 nsubjects    <- vect$nsubjects      # Extract the number of subjects
 variables    <- length(eigenvalues) # Compute the number of variables
 rep          <- 100                 # Number of replications for the parallel analysis
 cent         <- 0.95                # Centile value of the parallel analysis

## PARALLEL ANALYSIS (qevpea for the centile criterion, mevpea for the mean criterion)
 aparallel    <- parallel(var     = variables,
                          subject = nsubjects,
                          rep     = rep,
                          cent    = cent
                          )$eigen$qevpea  # The 95 centile

## NOMBER OF FACTORS RETAINED ACCORDING TO DIFFERENT RULES
 results      <- nScree(x=eigenvalues, aparallel=aparallel)

 is.nScree(results)
 results
 summary(results)

## PLOT ACCORDING TO THE nScree CLASS
 plot(results)