data(nutrimouse)
X <- nutrimouse$gene
Y <- nutrimouse$lipid
MyResult.pls <- pls(X,Y, ncomp = 4)
set.seed(30)
## run it using RELEASE_3_10 & devel -- see why such a big discrepancy
perf.pls <- perf(MyResult.pls, validation = "Mfold", folds = 5,
progressBar = FALSE, nrepeat = 10)
perf.pls$measures$Q2.total
plot(perf.pls$Q2.total)
perf.pls$Q2.total
## these are not looking right in the vignette -- the optimal should have
## maximum cor
# tuning both X and Y
set.seed(30) # for reproducibility in this vignette, otherwise increase nrepeat
tune.spls.cor.XY <- tune.spls(X, Y, ncomp = 3,
test.keepX = c(8, 20, 50),
test.keepY = c(4, 8, 16),
validation = "Mfold", folds = 5,
nrepeat = 10, progressBar = FALSE,
measure = 'cor')
## visualise correlations
plot(tune.spls.cor.XY, measure = 'cor')
## visualise RSS
plot(tune.spls.cor.XY, measure = 'RSS')
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