I'm new to PLSR and have 1 response variable (iso.freq) and 6 explanatory variables (leaf traits).
I ran the following code:
df.IsoFreq <- plsr( iso.freq ~ lma + ldmc + tough + thick + carbon + nitrogen, scale = TRUE, ncomp = 6, validation = "LOO", method = "oscorespls", data = df)
then plotted the root mean squared error prediction code: plot(RMSEP(df.IsoFreq), legendpos = "bottomright")
Am I correct in choosing components 1 and 2 for my analyses?
Also, When i did PLSr in unscrambler, I got different VIP values than R. Any suggestions on what could have occured? In unscrambler is said that ldmc and carbon are not important (values less then .6) while in R, it says the variables are important (VIP values > 1). Please help, PLS squad- you're my only hope!
lma ldmc tough thick carbon nitrogen
Comp 1 0.325 1.105 1.33 0.238 1.19 1.199
Comp 2 0.707 1.033 1.14 0.849 1.10 1.093