Nothing
PCAiv <- function(Y, X, row.w = NULL, ncp = 5) {
if(is.null(row.w)) row.w <- rep(1, nrow(Y))
if(any(sapply(Y, FUN = function(x) !is.numeric(x) & !is.integer(x)))) stop("variables in Y should all be numeric")
if(any(sapply(X, FUN = function(x) !is.numeric(x) & !is.integer(x) & !is.factor(x)))) stop("variables in X should all be factor or numeric")
if(nrow(Y) != nrow(X)) stop("Y and X should have the same number of rows")
Ys <- data.frame(lapply(Y, function(x) (x - stats::weighted.mean(x,row.w)) / descriptio::weighted.sd(x,row.w)))
names(Ys) <- names(Y)
for(i in 1:ncol(X)) {
if(!is.factor(X[,i])) X[,i] <- (X[,i] - stats::weighted.mean(X[,i],row.w)) / descriptio::weighted.sd(X[,i],row.w)
}
lmiv <- function(y) {
df <- data.frame(y = y, X)
yhat <- stats::predict(stats::lm(y ~ . , data = df, weights = row.w))
return(yhat)
}
YHAT <- do.call("cbind.data.frame", lapply(Ys, lmiv))
df <- cbind.data.frame(YHAT, X)
qualsup <- which(sapply(X, is.factor))
if(length(qualsup)==0) qualsup <- NULL
quantsup <- which(sapply(X, function(x) is.numeric(x) | is.integer(x)))
if(length(quantsup)==0) quantsup <- NULL
res <- FactoMineR::PCA(df, scale.unit = FALSE, ncp = ncp, row.w = row.w, quali.sup = (ncol(Y)+qualsup), quanti.sup = (ncol(Y)+quantsup), graph = FALSE)
pca <- FactoMineR::PCA(Ys, scale.unit = FALSE, ncp = ncp, row.w = row.w, graph = FALSE)
res$ratio <- sum(res$eig[,"eigenvalue"]) / sum(pca$eig[,"eigenvalue"])
class(res) <- c("PCA", "PCAiv", "list")
return(res)
}
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