R/NEVALSGT1.R

In EFA.dimensions: Exploratory Factor Analysis Functions for Assessing Dimensionality

NEVALSGT1 <- function (data, corkind='pearson', Ncases=NULL, verbose=TRUE) {
# Number of eigenvalues > 1

data <- MISSING_DROP(data)

Nvars  <- ncol(data)

# set up cormat
cordat <- setupcormat(data, corkind=corkind, Ncases=Ncases)
cormat <- cordat$cormat
ctype  <- cordat$ctype
Ncases <- cordat$Ncases
datakind <- cordat$datakind

eigenvalues <- cbind(eigen(cormat) $values)

totvarexplNOROT <- VarianceExplained(eigenvalues)

NfactorsNEVALSGT1 <- 0
for (nev in 1:nrow(eigenvalues)) {if (eigenvalues[nev,] > 1) NfactorsNEVALSGT1 <- NfactorsNEVALSGT1 + 1}

if (verbose == TRUE) { 
	message('\n\nNUMBER OF EIGENVALUES > 1')

	if (datakind == 'correlations') message('\nThe entered data is a correlation matrix.') 
	
	message('\nNumber of cases in the data file = ', Ncases)
	
	message('\nNumber of variables in the data file = ', Nvars)

	message('\nSpecified kind of correlations for this analysis: ', ctype, '\n')
	
	message('\n\nTotal Variance Explained (Initial Eigenvalues):\n')
	print(round(totvarexplNOROT,2), print.gap=4)
	
	message('\nThe number of eigenvalues greater than one = ', NfactorsNEVALSGT1, '\n')
}

nevalsgt1Output <- list(NfactorsNEVALSGT1=NfactorsNEVALSGT1, totvarexplNOROT=totvarexplNOROT)

return(invisible(nevalsgt1Output))

message('\n')
}