perf() on pls unable to handle near zero variance features

[Max-Bladen]

🐞 Describe the bug:
When running the perf() function on a pls object (using any PLS mode), if there are features which have near zero variance (nzv), the following error is raised:

Error in Ypred[omit, , h] <- Y.hat[, , 1] :
number of items to replace is not a multiple of replacement length

This pub was raised by two users on the discourse forum:
https://mixomics-users.discourse.group/t/pls-and-diablo-tuning/742/4

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🔍 reprex results from reproducible example including sessioninfo():

library(mixOmics)

data("liver.toxicity")

# reducing number of features to reduce run time
X <- liver.toxicity$gene[, 1:1000]
Y <- liver.toxicity$clinic

# to reproduce error, we need to induce some features to have near zero variance
X[, c(1, 23, 62, 234, 789)] <- 0

pls.obg <- pls(Y, X, ncomp = 4)
#> Warning in cor(A[[k]], variates.A[[k]]): the standard deviation is zero
pls.perf.obj <- perf(pls.obg, validation = "Mfold", folds = 4, 
                     progressBar = F, 
                     nrepeat = 3)
#> Error in Ypred[omit, , h] <- Y.hat[, , 1]: number of items to replace is not a multiple of replacement length

^{Created on 2022-03-28 by the reprex package (v2.0.1)}

Session info

sessioninfo::session_info()
#> - Session info ---------------------------------------------------------------
#>  setting  value
#>  version  R version 4.1.2 Patched (2021-11-16 r81220)
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#>  ui       RTerm
#>  language (EN)
#>  collate  English_Australia.1252
#>  ctype    English_Australia.1252
#>  tz       Australia/Sydney
#>  date     2022-03-28
#>  pandoc   2.14.2 @ C:/Users/Work/AppData/Local/Pandoc/ (via rmarkdown)
#> 
#> - Packages -------------------------------------------------------------------
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🤔 Expected behavior:
The perf() function is built to handle nzv features. Hence, it should ideally raise a warning to the fact that features have nzv, but should be able to proceed properly.

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💡 Possible solution:
Error is occurring at line 542. Likely a result of Y.pred containing columns for every feature whereas Y.hat containing columns for all non-nzv features. Hence, adjusting this line to:

Ypred[omit, nzv.Y, h] = Y.hat[, , 1]

may solve the bug.