Posts Tagged ‘ "R" Chemometrics ’

PCA for NIR Spectra_part 001: "Plotting the loadings"

February 22, 2012
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PCA for NIR Spectra_part 001: "Plotting the loadings"

There are different algorithms to calculate the Principal Components (PCs). Kurt Varmuza & Peter Filzmozer explain  them in their book: “Introduction to Multivariate Statistical Analysis in Chemometrics”.I´m going to apply one of them, to...

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Standard Normal Variate (SNV)

February 19, 2012
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Standard Normal Variate (SNV)

(This article was first published on NIR-Quimiometría, and kindly contributed to R-bloggers) This is another pretreatment used quite often in Near Infrared to remove the scatter. It is applied to every spectrum individually. The average and standard deviation of all the data points for that spectra is calculated. Every data point of the spectra is substracted from the mean and...

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Plotting the “Mean Spectrum”

February 17, 2012
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Plotting the “Mean Spectrum”

(This article was first published on NIR-Quimiometría, and kindly contributed to R-bloggers) Mean spectrum calculation is important: To center a matrix of spectra, we subtract the mean spectrum, from every spectrum in the matrix. There are also many options to use the mean spectrum, like average subsamples. Let´s calculate and plot the mean spectra for the Yarn NIR Data:...

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NIR "Cross Validaton Statistics" with "R"

February 16, 2012
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NIR "Cross Validaton Statistics" with "R"

We have to check different options before to decide for one model:Configure different cross validations.Configure different math  treatments.Configure number of terms.With the Yarn NIR data, I have develop 4 models, for a simple exercise.Of course...

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"NIR Std. Dev. Spectra" with "R"

February 15, 2012
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"NIR  Std. Dev. Spectra" with "R"

(This article was first published on NIR-Quimiometría, and kindly contributed to R-bloggers) It is always good to look at the spectra from different points of view, before to develop a regression, this will help us to understand better our samples, to detect outliers, to check where the variability is, if that variability correlates with the constituent of interest (directly...

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"R" PLS Package: Multiple Scatter Correction (MSC)

February 12, 2012
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"R" PLS Package: Multiple Scatter Correction  (MSC)

MSC (Multiple Scatter Correction) is a Math treatment to correct the scatter in the spectra. The scatter is produced for different physical circumstances as particle size, packaging.Normally scatter make worse the correlation of the spectra with the constituent of interest.Almost all the chemometric software’s available include this math treatment and of course “R” have it as well in the...

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"R": Predicting a Test Set (Gasoline)

February 9, 2012
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"R": Predicting a Test Set (Gasoline)

> data(gasoline)> #60 spectra of gasoline (octane is the constituent) > #We divide the whole Set into a Train Set and a Test Set.> gasTrain<-gasoline> gasTest<-gasoline> #Let´s develop the PLSR with the Tain Set ...

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"R": PLS Regression (Gasoline) – 005

February 8, 2012
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"R": PLS Regression (Gasoline) – 005

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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"R": PLS Regression (Gasoline) – 004

February 7, 2012
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"R": PLS Regression (Gasoline) – 004

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...

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"R": PLS Regression (Gasoline) – 003

February 3, 2012
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"R": PLS Regression  (Gasoline) – 003

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...

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