Publications by jrcuesta
"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 better at the Cros...
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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 Comp 3 Comp 4 Comp 5 Comp 6 70.9656438 7.5943956 7.5871843 9.2537926 0.7201960 0.8472951 Comp 7...
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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> gasTest> #Let´s develop the PLSR with the Tain Set and LOO CV> gas1> summary(gas1)Data: X dimension: 50 401 Y dimension: 50 1Fit method: kernelplsNumber of components considered: 10VALIDA...
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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 treat...
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"NIR Std. Dev. Spectra" with "R"
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 or inverse),….. Chemometric software’s have the tools to do...
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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 we can check many combinations.As math treatment I choose the raw spectra and the spectra treated w...
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Plotting the “Mean Spectrum”
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: > yarn_mean > wavelength > matplot(wavelength,yarn_mean,lty=1,pch...
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Standard Normal Variate (SNV)
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 divided by the standard deviation. “R” has a function to...
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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 the Yarn spectra.Previously we have to center the X matrix, let´s call it Xc.> XcThe algorithm I´m go...
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PCA for NIR Spectra_part 002: "Score planes"
The idea of this post is to compare the score plots for the first 3 principal components obtained with the algorithm “svd” with the scores plot of other chemometric software (Win ISI in this case). Previously I had exported the yarn spectra to this software.Let´s first use the command “pairs”, to see in “R” the score plots for the...
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