(This article was first published on

**NIR-Quimiometría**, and kindly contributed to R-bloggers)In the previous post we plot the Cross Validation predictions with:

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

We can plot the fitted values instead with:

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

**Graphics are different:**

Of course, using “train” we get overoptimisc statistics and we should look better at the Cross Validation or to an independant test set for validation.

We decided 3 components to develop the PLS Regressions looking to the RMSEP plot. We can use other plots as the MSEP plot (changing RMSEP for MSEP), or to the RSQ plot.

**> plot(R2(gas1),legendpos = “topright”)**

We can see how after the 3th component becomes almost flat.

We can see it better with numbers:

**> R2(gas1)**

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

**-0.03419 0.23374 0.93684 0.97111 0.97474 0.97474**

**6 comps 7 comps 8 comps 9 comps 10 comps**

**0.97713 0.97914 0.97742 0.97453 0.97413**

We can see also the residuals in “R” for the different number of component (1, 2,…,10). In these values the calculation for the statistics are based.

These are the residual values for the PLSR model with 3 components:

**octane**

**1 0.100769634**

**2 0.369121232**

**3 0.251715938**

**4 -0.209263557**

**5 -0.473107996**

**6 0.158305081**

**7 0.080218313**

**8 -0.141445641**

**9 -0.099252992**

**10 -0.077775217**

**11 0.561603527**

**12 0.488456018**

**13 -0.023514480**

**14 -0.106796820**

**15 -0.015477061**

**16 0.010451476**

**17 0.547102944**

**18 0.215613857**

**19 -0.290225797**

**20 0.238646916**

**21 0.115224011**

**22 -0.219819205**

**23 -0.040436420**

**24 -0.313450043**

**25 -0.161174139**

**26 0.065222607**

**27 0.032299933**

**28 -0.120728914**

**29 -0.394899511**

**30 -0.116389549**

**31 -0.242168963**

**32 -0.100928743**

**33 -0.003314534**

**34 0.152746720**

**35 0.092815472**

**36 0.029039668**

**37 0.020761125**

**38 0.339468953**

**39 0.019163788**

**40 0.192727538**

**41 -0.077437540**

**42 -0.267717370**

**43 0.161465598**

**44 0.101965851**

**45 -0.022411411**

**46 -0.322253768**

**47 -0.272445813**

**48 -0.151183595**

**49 0.063073375**

**50 -0.001254795**

**51 0.008358151**

**52 0.297159695**

**53 0.015659145**

**54 0.033326901**

**55 -0.141411827**

**56 0.280361574**

**57 -0.491022823**

**58 -0.332150710**

**59 0.269220723**

**60 -0.082606528**

*Bibliography:*

*Tutorials PLS Package for “R” :*Norwegian University of Life Sciences

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