nScreeObjectMethods {nFactors} | 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': is (object) ## S3 method for class 'nScree': plot (x, ...) ## S3 method for class 'nScree': print (x, ...) ## S3 method for class 'nScree': summary(object, ...)
x |
nScree: an object of the class nScree |
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 |
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 plotnScre
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 |
Gilles Raiche
Centre sur les Applications des Modeles de Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/
Raiche, G., Riopel, M. and Blais, J.-G. (2006). Non graphical solutions for the Cattell's scree test. Paper presented at the International Annual meeting of the Psychometric Society, Montreal. [http://www.er.uqam.ca/nobel/r17165/RECHERCHE/COMMUNICATIONS/]
plotuScree
,
plotnScree
,
parallel
,
plotParallel
,
## 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)