Nothing
"scoreOverlap" <-
function(keys,r,correct=TRUE,SMC=TRUE,av.r=TRUE,item.smc=NULL,impute=TRUE,select=TRUE,scores=FALSE, min=NULL,max=NULL) { #function to score clusters according to the key matrix, correcting for item overlap
#minor fix on 01/11/24 to handle the case of one key
tol=sqrt(.Machine$double.eps) #machine accuracy
cl <- match.call()
bad <- FALSE
key.list <- keys #we need to keep this as a list for later
if(is.list(keys) & (!is.data.frame(keys))) { if (select) {
select <- selectFromKeyslist(colnames(r),keys)
# select <- sub("-","",unlist(keys)) #added April 7, 2017
select <- select[!duplicated(select)]
} else {select <- 1:ncol(r) }
if (!isCorrelation(r,na.rm=TRUE)) {if(scores) {items <- r[,select]} else {items <- NULL} #save the data for scoring
r <- cor(r[,select],use="pairwise")} else {r <- r[select,select]}
keys <- make.keys(r,keys)} #added 9/9/16 (and then modified March 4, 2017
if(!is.matrix(keys)) keys <- as.matrix(keys) #keys are sometimes a data frame - must be a matrix
if ((dim(r)[1] != dim(r)[2]) ) {r <- cor(r,use="pairwise")}
if(any(abs(r[!is.na(r)]) > 1)) warning("Something is seriously wrong with the correlation matrix, some correlations had absolute values > 1! Please check your data.")
if(any(is.na(r))) {
# SMC=FALSE
# warning("Missing values in the correlation matrix do not allow for SMC's to be found")
bad <- TRUE}
if(SMC && is.null(item.smc)) {item.smc <- smc(r)} else {
diag(r) <- NA
item.smc <- apply(r,1,function(x) max(abs(x),na.rm=TRUE))
item.smc[is.infinite(item.smc) ] <- 1
diag(r) <- 1}
if(all(item.smc ==1)) SMC <- FALSE
if(!bad) {covar <- t(keys) %*% r %*% keys} else #matrix algebra is our friend
{#covar<- apply(keys,2,function(x) colSums(apply(keys,2,function(x) colSums(r*x,na.rm=TRUE))*x,na.rm=TRUE)) #matrix multiplication without matrices!
covar <- score.na(keys,r,cor=FALSE)
}
var <- diag(covar) #these are the scale variances
n.keys <- ncol(keys)
item.var <- item.smc
raw.r <- cov2cor(covar)
key.var <- diag(t(keys) %*% keys)
key.smc <- t(keys) %*% item.smc
key.alpha <- ((var-key.var)/var)*(key.var/(key.var-1))
key.lambda6 <- (var - key.var + key.smc)/var
key.alpha[is.nan(key.alpha)] <- 1 #if only 1 variable to the cluster, then alpha is undefined
key.alpha[!is.finite(key.alpha)] <- 1
key.av.r <- key.alpha/(key.var - key.alpha*(key.var-1)) #alpha 1 = average r
colnames(raw.r) <- rownames(raw.r) <- colnames(keys)
names(key.lambda6) <- colnames(keys)
key.lambda6 <- drop(key.lambda6)
n.keys <- ncol(keys)
sn <- key.av.r * key.var/(1-key.av.r)
if(!bad) { item.cov <- t(keys) %*% r #the normal case is to have all correlations
raw.cov <- item.cov %*% keys} else {
item.cov <- apply(keys,2,function(x) colSums(r*x,na.rm=TRUE)) #some correlations are NA have to adjust
raw.cov <- apply(keys,2,function(x) colSums(item.cov*x,na.rm=TRUE))
item.cov <- t(item.cov)
}
adj.cov <- raw.cov
#now adjust them
med.r <- rep(NA, n.keys)
for (i in 1:(n.keys)) {
temp <- keys[,i][abs(keys[,i]) > 0]
temp <- diag(temp,nrow=length(temp))
small.r <- r[abs(keys[,i])>0,abs(keys[,i])>0]
#small.r <- temp %*% small.r %*% temp #this is just flipping the signs, but will not work with missing data
if(NROW(temp) > 1) small.r <- apply(temp,2, function(x) colSums(apply(temp,2, function(x) colSums(small.r * x,na.rm=TRUE))*x,na.rm=TRUE))
med.r[i] <- median(small.r[lower.tri(small.r)],na.rm=TRUE)
for (j in 1:i) {
if(av.r) { adj.cov[i,j] <- adj.cov[j,i]<- raw.cov[i,j] - sum(keys[,i] * keys[,j] ) + sum(keys[,i] * keys[,j] * sqrt(key.av.r[i] * key.av.r[j]))
} else {
adj.cov[i,j] <- adj.cov[j,i] <- raw.cov[i,j] - sum(keys[,i] * keys[,j] )+ sum( keys[,i] * keys[,j] * sqrt(item.smc[i]* abs(keys[,i])*item.smc[j]*abs(keys[,j]) ))
}
} }
scale.var <- diag(raw.cov)
diag(adj.cov) <- diag(raw.cov)
adj.r <- cov2cor(adj.cov) #this is the overlap adjusted correlations
#find the MIMS values (Average within cluster/scale items)
scale.size <- outer(key.var,key.var)
MIMS <- adj.cov/scale.size
diag(MIMS)<- key.av.r
#adjust the item.cov for item overlap
#we do this by replacing the diagonal of the r matrix with the item.var (probably an smc, perhaps a maximum value)
diag(r) <- item.var
if(!bad) { item.cov <- t(keys) %*% r #the normal case is to have all correlations
} else {
item.cov <- t(apply(keys,2,function(x) colMeans(r*x,na.rm=TRUE)) *NROW(keys)) #some correlations are NA
}
if(n.keys > 1) {
item.cor <- sqrt(diag(1/(key.lambda6*scale.var))) %*% (item.cov) # %*% diag(1/sqrt(item.var))
rownames(item.cor) <- colnames(keys)
colnames(item.cor) <- colnames(r)
} else {
item.cor <- r %*% keys /sqrt(key.lambda6*scale.var) }
item.cor <- t(item.cor)
names(med.r) <- colnames(keys)
#find the Multi-Item Multi Trait item x scale correlations
#this only makes sense if n.keys > 1
MIMT <- matrix(NA,n.keys,n.keys)
for (i in 1:(n.keys)) {
temp <- keys[,i][abs(keys[,i]) > 0]
if(n.keys > 1){ flip.item <- temp * item.cor[names(temp),,drop=FALSE]} else {flip.item <- temp * item.cor[names(temp)]}
if(length(names(temp)) > 1) { if(n.keys >1) {MIMT[i,] <- colMeans(item.cor[names(temp),])}} else {MIMT[i,] <- flip.item}
}
colnames(MIMT) <- rownames(MIMT) <- colnames(keys)
good <- scale_quality(adj.r,item.cor,key.list)
names(good) <- names(key.list)
if(scores) {
abskeys <- abs(keys)
num.item <- diag(t(abskeys) %*% abskeys) #how many items in each scale
num.ob.item <- num.item #will be adjusted in case of impute = FALSE
n.subjects <- dim(items)[1]
item.means <- colMeans(items,na.rm=TRUE)
if (is.null(min)) {min <- min(items,na.rm=TRUE)}
if (is.null(max)) {max <- max(items,na.rm=TRUE)}
if(impute !="none") {
miss <- which(is.na(items),arr.ind=TRUE)
if(impute=="mean") {
item.means <- colMeans(items,na.rm=TRUE) #replace missing values with means
items[miss]<- item.means[miss[,2]]} else {
item.med <- apply(items,2,median,na.rm=TRUE) #replace missing with medians
items[miss]<- item.med[miss[,2]]} #this only works if items is a matrix
scores <- items %*% keys #this actually does all the work but doesn't handle missing values
C <- cov(items,use="pairwise")
cov.scales <- cov(scores,use="pairwise") #and total scale variance
cov.scales2 <- diag(t(abskeys) %*% C^2 %*% abskeys) # sum(C^2) for finding ase
} else { #handle the case of missing data without imputation
scores <- matrix(NaN,ncol=n.keys,nrow=n.subjects)
#we could try to parallelize this next loop
for (scale in 1:n.keys) {
pos.item <- items[,which(keys[,scale] > 0)]
neg.item <- items[,which(keys[,scale] < 0)]
neg.item <- max + min - neg.item
sub.item <- cbind(pos.item,neg.item)
scores[,scale] <- rowMeans(sub.item,na.rm=TRUE)
rs <- rowSums(!is.na(sub.item))
num.ob.item[scale] <- mean(rs[rs>0]) #added Sept 15, 2011
# num.ob.item[scale] <- mean(rowSums(!is.na(sub.item))) # dropped
} # end of scale loop
# we now need to treat the data as if we had done correlations at input
}
colnames(scores)<- names(key.list)
} #end of if scores loop
if (correct) {cluster.corrected <- correct.cor(adj.r,t(key.alpha))
result <- list(cor=adj.r,sd=sqrt(var),corrected= cluster.corrected,alpha=key.alpha,av.r = key.av.r,size=key.var,sn=sn,G6 =key.lambda6, item.cor=item.cor, med.r=med.r,quality=good, MIMS=MIMS,MIMT=MIMT,scores=scores,Call=cl)
} #correct for attenuation
else {
result <- list(cor=adj.r,sd=sqrt(var),alpha=key.alpha, av.r = key.av.r,
size=key.var,sn=sn,G6 =key.lambda6, item.cor=item.cor, med.r=med.r, scores=scores, Call=cl)}
class(result) <- c ("psych", "overlap")
return(result)}
#modified 01/11/15 to find r if not a square matrix
#modifed 03/05/15 to do pseudo matrix multiplication in case of missing data
scale_quality = function(phi,r,keys) { #switched from . to _ 6/20/23
nvar <- NROW(r)
nscale <- NCOL(r)
good <- rep(0,length(keys))
best <- apply(abs(r),1, which.max)
for(i in 1:length(keys)) {
select <- selectFromKeys(keys[i])
good [i] <- sum(best[select] == i)
good[i] <- good[i]/length(select)
}
return(good)
}
# scale.quality = function(n.obs,phi,r,keys) {
# nvar <- NROW(r)
# nscale <- NCOL(r)
# best <- good <- rep(0,length(keys))
# best <- apply(abs(r),1, which.max)
# for(i in 1:length(keys)) {
# select <- selectFromKeys(keys[i])
# good [i] <- sum(best[select] == i)
# }
# return(good)
# }
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