nScreeObjectMethods function

Utility Functions for nScree Class Objects

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.

## 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

Returns

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

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)

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.

Author(s)

Gilles Raiche

Centre sur les Applications des Modeles de Reponses aux Items (CAMRI)

Universite du Quebec a Montreal

raiche.gilles@uqam.ca