# "R": PLS Regression (Gasoline) – 003

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The gasoline data set has the spectra of 60 samples acquired by diffuse reflectance from 900 to 1700 nm. We saw how to plot the spectra in the

**previous post**.Now, following the tutorial of Bjorn-Helge Mevik published in “R-News Volume 6/3, August 2006”, we will do the PLS regression:

**gas1 <- plsr(octane~NIR, ncomp = 10,data = gasoline, validation = "LOO")**

This will fit a model of 10 components.

We will use the “Leave one out Cross Validation” (LOO)

The constituent is the octane number.

**> summary(gas1)**

Data: X dimension: 60 401

Y dimension: 60 1

Fit method: kernelpls

Number of components considered: 10

VALIDATION: RMSEP

Cross-validated using 60 leave-one-out segments.

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

CV 1.543 1.328 0.3813 0.2579 0.2412 0.2412 0.2294

adjCV 1.543 1.328 0.3793 0.2577 0.2410 0.2405 0.2288

7 comps 8 comps 9 comps 10 comps

CV 0.2191 0.2280 0.2422 0.2441

adjCV 0.2183 0.2273 0.2411 0.2433

TRAINING: % variance explained

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

X 70.97 78.56 86.15 95.40 96.12 96.97 97.32 98.10

octane 31.90 94.66 97.71 98.01 98.68 98.93 99.06 99.11

9 comps 10 comps

X 98.32 98.71

octane 99.20 99.24

One way to decide better the number of components to use, is to plot the RMSEPs:

**> plot(RMSEP(gas1), legendpos = “topright”)**

**adjCV**is the RMSEP Bias corrected which in the case of “LOO” is almost the same that the RMSEP without correction.

The plot suggest three components giving a RMSEP of 0.258.

Now we can see the different plots like the prediction plot:

**> plot(gas1, ncomp = 3, asp = 1, line = TRUE)**

We will continue with more plots in the next post.

**Bibliography:****Tutorials of :****Bjorn-Helge Mevik**

*
*

Norwegian University of Life Sciences

**Ron Wehrens**

Radboud University Nijmegen

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