Using correlation in LOCAL (Resemble package)

> ctrl.mbl <- mblControl(sm = "cor",

                  pcSelection = list("cumvar", 0.999),
                  valMethod = "NNv",
                  scaled = FALSE, center = TRUE)

 > local.mbl <- mbl(Yr = Yr, Xr = Xr, Yu = Yu, Xu = Xu,

mblCtrl = ctrl.mbl,

                   dissUsage = "none",

                   k = seq(40, 150, by = 10),

                   pls.c = c(5, 15),

                   method = "wapls1")

 

Predicting sample: 1  ----------
Predicting sample: 2  ----------
Predicting sample: 3  ----------
Predicting sample: 4  ----------
Predicting sample: 5  ----------

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> plot(predicted.local,Yu)

  

 

 

This time I use correlation (as Win ISI use) and try to find the best number of samples to select for the LOCAL algorithm with a sequence. 

As we can see the predictions improve with more samples in the calibration (red dots), maybe could be better win more samples by at the end of the plot it start to stabilize.