"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) # }