13 dic. 2015

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)

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