ex1 <- mbl(Yr = Y_train, Xr = X_train, Yu = NULL, Xu = X_val,
mblCtrl = ctrl,
distUsage = "predictors",
k = seq(30, 150, 15),
method = "wapls1",
pls.c = c(7, 20))
the predictions for every spectra in the NIRsoil validation set.
Now we can use the function "plot.mbl":
plot(x, g = c("validation", "pca"), param = "rmse", pcs = c(1,2), ..)to look to some interesting plots that help us to understand better the performance of the different models:
The first plot shows the errors (RMSE) with the different configurations of neighbors:
The next plot shows the Score plot (PC1 vs PC2) with the training samples (Xr) in one color and the validation samples (Xu) in a different color.
We can see with also the correlation plot, changing the param for "r2" indeed "rmse".