summary.nScree | R Documentation |
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)
object |
nScree: an object of the class |
... |
variable: additionnal parameters to give to the |
x |
Results of a previous |
Generic functions for the nScree class:
is.nScree |
logical: is the object of the class |
plot.nScree |
graphic: plots a figure according to the
|
print.nScree |
numeric: vector of the
number of components/factors to retain: same as the |
summary.nScree |
data.frame: details
of the results from a nScree analysis: same as the |
Gilles Raiche
Centre sur les Applications des Modeles de
Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca
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.
## 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)
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