joaovissoci · October 5, 2023 08:11 · chrissie1997 · Oct 5, 2023
diff --git a/kmo_function.R b/kmo_function.R
 #KMO
 kmo = function( data ){
 	
 	library(MASS)
 	X <- cor(as.matrix(data))
 	iX <- ginv(X)
 	S2 <- diag(diag((iX^-1)))
 	AIS <- S2%*%iX%*%S2                      # anti-image covariance matrix
 	IS <- X+AIS-2*S2                         # image covariance matrix
 	Dai <- sqrt(diag(diag(AIS)))
 	IR <- ginv(Dai)%*%IS%*%ginv(Dai)         # image correlation matrix
 	AIR <- ginv(Dai)%*%AIS%*%ginv(Dai)       # anti-image correlation matrix
 	a <- apply((AIR - diag(diag(AIR)))^2, 2, sum)
 	AA <- sum(a)
 	b <- apply((X - diag(nrow(X)))^2, 2, sum)
 	BB <- sum(b)
 	MSA <- b/(b+a)                        # indiv. measures of sampling adequacy
 	
 	AIR <- AIR-diag(nrow(AIR))+diag(MSA)  # Examine the anti-image of the
 	# correlation matrix. That is the
 	# negative of the partial correlations,
 	# partialling out all other variables.
 	
 	kmo <- BB/(AA+BB)                     # overall KMO statistic
 	
 	# Reporting the conclusion
 	if (kmo >= 0.00 && kmo < 0.50){
 		test <- 'The KMO test yields a degree of common variance
 		unacceptable for FA.'
 	} else if (kmo >= 0.50 && kmo < 0.60){
 		test <- 'The KMO test yields a degree of common variance miserable.'
 	} else if (kmo >= 0.60 && kmo < 0.70){
 		test <- 'The KMO test yields a degree of common variance mediocre.'
 	} else if (kmo >= 0.70 && kmo < 0.80){
 		test <- 'The KMO test yields a degree of common variance middling.'
 	} else if (kmo >= 0.80 && kmo < 0.90){
 		test <- 'The KMO test yields a degree of common variance meritorious.'
 	} else {
 		test <- 'The KMO test yields a degree of common variance marvelous.'
 	}
 	
 	ans <- list(  overall = kmo,
 								report = test,
 								individual = MSA,
 								AIS = AIS,
 								AIR = AIR )
 	return(ans)
 	
 }    # end of kmo()
	#KMO
	kmo = function( data ){

	library(MASS)
	X <- cor(as.matrix(data))
	iX <- ginv(X)
	S2 <- diag(diag((iX^-1)))
	AIS <- S2%%iX%%S2 # anti-image covariance matrix
	IS <- X+AIS-2*S2 # image covariance matrix
	Dai <- sqrt(diag(diag(AIS)))
	IR <- ginv(Dai)%%IS%%ginv(Dai) # image correlation matrix
	AIR <- ginv(Dai)%%AIS%%ginv(Dai) # anti-image correlation matrix
	a <- apply((AIR - diag(diag(AIR)))^2, 2, sum)
	AA <- sum(a)
	b <- apply((X - diag(nrow(X)))^2, 2, sum)
	BB <- sum(b)
	MSA <- b/(b+a) # indiv. measures of sampling adequacy

	AIR <- AIR-diag(nrow(AIR))+diag(MSA) # Examine the anti-image of the
	# correlation matrix. That is the
	# negative of the partial correlations,
	# partialling out all other variables.

	kmo <- BB/(AA+BB) # overall KMO statistic

	# Reporting the conclusion
	if (kmo >= 0.00 && kmo < 0.50){
	test <- 'The KMO test yields a degree of common variance
	unacceptable for FA.'
	} else if (kmo >= 0.50 && kmo < 0.60){
	test <- 'The KMO test yields a degree of common variance miserable.'
	} else if (kmo >= 0.60 && kmo < 0.70){
	test <- 'The KMO test yields a degree of common variance mediocre.'
	} else if (kmo >= 0.70 && kmo < 0.80){
	test <- 'The KMO test yields a degree of common variance middling.'
	} else if (kmo >= 0.80 && kmo < 0.90){
	test <- 'The KMO test yields a degree of common variance meritorious.'
	} else {
	test <- 'The KMO test yields a degree of common variance marvelous.'
	}

	ans <- list( overall = kmo,
	report = test,
	individual = MSA,
	AIS = AIS,
	AIR = AIR )
	return(ans)

	} # end of kmo()