[R] PLS in R

[Margarida Soares]

Hello, I need help with a partial least square regression in R. I have read
both the vignette and the post on R bloggers but it is hard to figure out
how to do it. Here is the script I wrote:

library(pls)
plsrcue<- plsr(cue~fb+cn+n+ph+fung+bact+resp, data = cue, ncomp=7,
na.action = NULL, method = "kernelpls", scale=FALSE, validation = "LOO",
model = TRUE, x = FALSE, y = FALSE)
summary(plsrcue)

and I got this output, where I think I can choose the number of components
based on RMSEP, but how do I choose it?

Data:  X dimension: 33 7
Y dimension: 33 1
Fit method: kernelpls
Number of components considered: 7

VALIDATION: RMSEP
Cross-validated using 33 leave-one-out segments.
       (Intercept)  1 comps  2 comps  3 comps  4 comps  5 comps  6 comps  7
comps
CV         0.09854  0.07014  0.05366  0.04712  0.01935  0.01943  0.01882
 0.01900
adjCV      0.09854  0.06999  0.05357  0.04703  0.01930  0.01942  0.01876
 0.01893

TRAINING: % variance explained
     1 comps  2 comps  3 comps  4 comps  5 comps  6 comps  7 comps
X      42.33    78.82    99.15    99.95   100.00   100.00   100.00
cue    56.77    76.14    81.98    97.05    97.11    97.56    97.75


- and also, how to proceed from here?

- and how to make a correlation plot?

- what to do with the values, coefficients that I get in the Environment
(pls values)


Thanks for your help!

margarida soares

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