PLS prediction if X is centered (using f.e. R package chemometrics)

Question

General question about predictions in PLS, using the chemometrics package in R here for an example. The standard example for pls1_nipals is

data(PAC)
res <- pls1_nipals(PAC$X,PAC$y,a=5)

This returns the coefficient vector res$b. Now lets assume the first 200 of 209 objects (rows) of PAC$X,PAC$y are used for calibration and the rest is used for validation.

res <- pls1_nipals(PAC$X[1:200,],PAC$y[1:200],a=5)
cbind(PAC$X[201:209,]%*%res$b,PAC$y[201:209])

leads to

    [,1]   [,2]
470103.8 486.81
457944.5 488.18
491483.9 495.01
487573.0 495.45
499962.8 497.66
487223.3 500.00
656960.3 501.32
661962.2 503.89
635861.9 503.91

The reason for the extreme difference is, that X and Y were centered. The centering of Y can be eliminated bei adding mean(PAC$[1:200]) to PAC$X[201:209,]%*%res$b. But how to correct the centering of X? First I thought by using colMeans(PAC$X[1:200,]), but it didn't work.

cbind((PAC$X[201:209,]
              -matrix(rep(colMeans(PAC$X[1:200,]),9),ncol=467,nrow=9,byrow=TRUE))
              %*%res$b+mean(PAC$y[1:200]),
      PAC$y[201:209])

Any ideas? Thanks.

Answer 1

You really found a bug, which was immediately fixed by the maintainer (version 1.4.1 on CRAN - available already as .tar.gz, win binaries will take a bit longer):

library (chemometrics)
library (pls)

train <- yarn [yarn$train,]

res <- pls1_nipals(train$NIR, train$density, a = 2)
x.centered <- scale (test$NIR, center = colMeans (train$NIR), scale = FALSE)
y.center <- mean (train$density)
y.hat <- x.centered %*% res$b + y.center

For comparison, set up the (kernel)PLS model using package pls:

res.pls <- plsr (density ~ NIR, data = train, ncomp = 2)
y.pls <- predict (res.pls, data = yarn [! yarn$train])

now we have:

> cbind (y.hat, y.pls, y.hat - y.pls)
              density       density
110 53.09713 53.09713  0.000000e+00
22  52.74115 52.74115 -8.526513e-14
31  34.67483 34.67483 -8.526513e-14
41  37.48693 37.48693 -7.105427e-15
51  32.26085 32.26085 -3.552714e-14
61  21.23447 21.23447 -6.394885e-14
71  22.18097 22.18097 -4.973799e-14 

So basically the same (up to "numeric noise")