Let´s see know how to plot the scores for the 3 PLS Components: We can see the explained variance from each component in the diagonal.We can get it from R with:> explvar(gas1) Comp 1 Comp 2 &nbs...

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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...

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...

As other softwares "R" has nice tools to look to the data before to develop the calibration.Statistics for the "Y" variable (in this case octane number) like Maximun, Minimun,..,standard deviation,...are important:> library(ChemometricsWithR)> data(gasoline)> summary(gasoline$octane) Min. 1st Qu. Median Mean 3rd Qu. Max. 83.40 85.88 87.75 87.18 88.45 89.60> sd(gasoline$octane) 1.530078And of course the Histogram:> hist(gasoline$octane)

The 8th international R users conference useR! 2012 will be in Nashville TN USA June 12-15 with a special all-day pre-conference course from Bill Venables on June 11. We have a terrific lineup of half-day tutorials on June 12 and will have invited and contributed presentations of interest to a wide variety of R users. Details may be found...

How to create the best Interactive R Language Online Learning Platform from the views of R community?: R offers a breadth and depth in statistical computing beyond what is available in commercial closed source products. Yet R remains, primarily, a ...

Update!: The latest version of Revolution R, which added support for RHEL 6, appears to work (it appears to at least install, run, and perform basic tasks). See this post for more details. I’ve come to enjoy using R. I had dabbled with it in the past, but found it painfully opaque, and the Effort:Reward

(This article was first published on R-statistics blog » R, and kindly contributed to R-bloggers) The followings introductory post is intended for new users of R. It deals with interactive visualization using R through the iplots package. This is a guest article by Dr. Robert I. Kabacoff, the founder of (one of) the first online R tutorials websites: Quick-R. Kabacoff has...