nCng {nFactors} | R Documentation |
This function computes the CNG indices for the eigenvalues of a correlation/covariance matrix (Gorsuch and Nelson, 1981; Nasser, 2002, p. 400; Zoski and Jurs, 1993, p. 6).
nCng(x, cor=TRUE, model="components", details=TRUE, ...)
x |
numeric: a vector of eigenvalues, a matrix of
correlations or of covariances or a data.frame of data |
cor |
logical: if TRUE computes eigenvalues from a correlation
matrix, else from a covariance matrix |
model |
character: "components" or "factors" |
details |
logical: if TRUE also return detains about the computation
for each eigenvalues. |
... |
variable: additionnal parameters to give to the eigenComputes
function |
Note that the nCng
function is only valid when more than six eigenvalues
are used and that these are obtained in the context of a principal components
analysis. For a factor analysis, some eigenvalues could be negative and
the function will stop and give an error message.
The slope of all possible sets of three adjacent eigenvalues are compared, so CNG indices can be applied when more than six eigenvalues are used. The eigenvalue at which the greatest difference between two successive slopes occurs is the indicator of the number of components/factors to retain.
nFactors |
numeric: number of factors retained by the CNG procedure. |
details |
numeric: matrix of the details for each indice. |
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/
Gorsuch, R. L. and Nelson, J. (1981). CNG scree test: an objective procedure for determining the number of factors. Presented at the annual meeting of the Society for multivariate experimental psychology.
Nasser, F. (2002). The performance of regression-based variations of the visual scree for determining the number of common factors. Educational and Psychological Measurement, 62(3), 397-419.
Zoski, K. and Jurs, S. (1993). Using multiple regression to determine the number of factors to retain in factor analysis. Multiple Linear Regression Viewpoints, 20(1), 5-9.
plotuScree
,
nScree
,
plotnScree
,
plotParallel
## SIMPLE EXAMPLE OF A CNG ANALYSIS data(dFactors) eig <- dFactors$Raiche$eigenvalues results <- nCng(eig, details=TRUE) results plotuScree(eig, main=paste(results$nFactors, " factors retained by the CNG procedure", sep=""))