Practice with Cereal Data (Chemometric package)

This is a post to follow the example of the cereal data in the book “Introduction to multivariate Statistical Analysis in Chemometrics” (Kurt Varmuza & Peter Filzmoser).

PLS2 is applied to a Cereal Data set with the function “mvr” using the cross validation “LOO: leave one out”.

The validation plot shows the error of the validation vs the number o PLS terms, for the different constituents.

A for every constituent the number of terms choose can be different, so an average is recommended.

This plot shows the error of the validation vs the number o PLS terms, for all the constituents overlapped.

As we can see, we choose 7 components for the model.

We can see the XY plots (reference vs predicted) with 7 terms for all the constituents.

pred7<-predict(respls2, cereal, ncomp = 7,type = c("response"))

par(mfrow=c(2,3))

     plot(pred_7$Heat_Ref,pred_7$Heat_Pred)

     plot(pred_7$C_Ref,pred_7$C_Pred)

     plot(pred_7$H_Ref,pred_7$H_Pred)

     plot(pred_7$N_Ref,pred_7$N_Pred)

     plot(pred_7$Starch_Ref,pred_7$Starch_Pred)

     plot(pred_7$Ash_Ref,pred_7$Ash_Pred)