I understand from these graphs that 4 components can make a strong model. However, I want to know

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Partial Least square regression model (PLS) for Budle Bay meadow 

I am investigating if climatic variables (max temp, rainfall, and solar radiation) are influencing seagrass percent cover. This example is for one meadow. I am using a partial least square regression model to investigate this. 

###make this example reproducible

set.seed(1)

### pls model

model1 <- plsr(budle_bay_pls_$cover~budle_bay_pls_$`sunshine 01`+budle_bay_pls_$`sunshine 03`+budle_bay_pls_$`sunshine 06`+budle_bay_pls_$`sunshine 09`+budle_bay_pls_$`sunshine 12`+budle_bay_pls_$`max temp 01`+budle_bay_pls_$`max temp 03`+budle_bay_pls_$`max temp 06`+budle_bay_pls_$`max temp 09`+budle_bay_pls_$`max temp 12`+budle_bay_pls_$`rainfall 01`+budle_bay_pls_$`rainfall 03`+budle_bay_pls_$`rainfall 06`+budle_bay_pls_$`rainfall 09`+budle_bay_pls_$rainfall12, data=budle_bay_pls_, scale=TRUE, validation="CV")

summary(model1)

Data: X dimension: 10 15 

Y dimension: 10 1

Fit method: kernelpls

Number of components considered: 8

VALIDATION: RMSEP

Cross-validated using 10 leave-one-out segments.

       (Intercept)  1 comps  2 comps  3 comps  4 comps  5 comps  6 comps  7 comps  8 comps

CV           18.02    19.79    19.85    18.55    16.57    16.48    17.19    17.37    17.14

adjCV        18.02    19.25    19.09    17.85    15.83    15.73    16.33    16.49    16.26

 

TRAINING: % variance explained

                      1 comps  2 comps  3 comps  4 comps  5 comps  6 comps  7 comps  8 comps

X                       19.67    41.83    56.42    65.25    75.83    84.03    95.79    98.53

budle_bay_pls_$cover    61.94    81.26    88.81    95.79    97.59    99.43    99.83    99.98

I understand from these graphs that 4 components can make a strong model. However, I want to know which components they are, and I am not sure how to do that. I have played around with the order I have written the code and that does not seem to change anything. 

My thoughts are to make a graph like the one below however this is for a PCA analysis and not a PLS analysis. Therefore, when I try follow a method to make a graph like this it does not work. 

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