Change the function name

NAMESPACE

@@ -8,8 +8,8 @@ export(getELBO)
 export(getImportance)
 export(initSF)
 export(predictFX)
-export(softImputeCV)
-export(softImputeCV_sparse)
+export(softImputeCrossVal)
+export(softImputeCrossValSparse)
 export(updateMFAIR)
 exportClasses(MFAIR)
 exportClasses(MFAIRSingleFactor)

R/softImputeCV.R

@@ -11,10 +11,10 @@
 #' @return A list containing the cross-validation results.
 #' @export
 #'
-softImputeCV <- function(Y, rank_max = NULL, lambda_range = NULL,
- nfold = 10, para_length = 100) {
- N <- dim(Y)[1]
- M <- dim(Y)[2]
+softImputeCrossVal <- function(Y, rank_max = NULL, lambda_range = NULL,
+  nfold = 10, para_length = 100) {
+ N <- nrow(Y)
+ M <- ncol(Y)
  # Report settings
  message("Info: Matrix dimension: ", N, " * ", M)
  message("Info: Number of cv folds: ", nfold)
@@ -103,10 +103,10 @@ softImputeCV <- function(Y, rank_max = NULL, lambda_range = NULL,
 #' @return A list containing the cross-validation results.
 #' @export
 #'
-softImputeCV_sparse <- function(Y, rank_max = NULL, lambda_range = NULL,
- nfold = 10, para_length = 100) {
- N <- dim(Y)[1]
- M <- dim(Y)[2]
+softImputeCrossValSparse <- function(Y, rank_max = NULL, lambda_range = NULL,
+  nfold = 10, para_length = 100) {
+ N <- nrow(Y)
+ M <- ncol(Y)
  # Report settings
  message("Info: Matrix dimension: ", N, " * ", M)
  message("Info: Number of cv folds: ", nfold)
@@ -152,9 +152,6 @@ softImputeCV_sparse <- function(Y, rank_max = NULL, lambda_range = NULL,
  j = idx_all[train_idx, 2],
  x = Y@x[train_idx] - train_mean
  )
- # Y_test <- Incomplete(i = idx_all[-train_idx, 1],
- # j = idx_all[-train_idx, 2],
- # x = Y@x[-train_idx])
 
  for (j in seq(from = para_length, to = 1, by = -1)) {
  if (j == para_length) {
@@ -170,10 +167,11 @@ softImputeCV_sparse <- function(Y, rank_max = NULL, lambda_range = NULL,
  )
  }
 
+ # The prediction corresponding to the test set
  Y_hat <- impute(
  si_fit,
  i = idx_all[-train_idx, 1], j = idx_all[-train_idx, 2]
- ) + train_mean # The prediction cooresponding to the test set
+ ) + train_mean
  test_err[i, j] <- sqrt(mean((Y@x[-train_idx] - Y_hat)^2))
  }
  }