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PR addressing Issue #161 #175

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PR addressing Issue #161 #175

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18 changes: 18 additions & 0 deletions R/tune.spca.R
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Expand Up @@ -76,6 +76,24 @@ tune.spca <- function(X,
## split data to train/test
X.train = X[-test.fold.inds,,drop=FALSE]
X.test = X[test.fold.inds,,drop=FALSE]



# find indices of rows in X.train that contain NAs
rows.NAs.train <- rowSums(is.na(X.train))
rows.NAs.train <- which(rownames(X.train) %in% names(rows.NAs.train[rows.NAs.train!= 0]))

# find indices of rows in X.test that contain NAs
rows.NAs.test <- rowSums(is.na(X.test))
rows.NAs.test <- which(rownames(X.test) %in% names(rows.NAs.test[rows.NAs.test!= 0]))

# if there are rows with NAs, remove them from the appropriate sets
if (length(rows.NAs.train)>0) {X.train <- X.train[-rows.NAs.train,]}
if (length(rows.NAs.test)>0) {
X.test <- X.test[-rows.NAs.test,]
t.comp.actual <- t.comp.actual[-rows.NAs.test]
}

# ---- run sPCA
## train
spca.train = spca(X.train, ncomp = ncomp, keepX = c(keepX.opt, keepX.value), center = center, scale = FALSE)
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