This time I´m going to use my own data to develop a model to predict some fatty acid in the solid fat (pork).Samples had been analyzed in a NIT (Near Infrared Transmittance) instrument. The range of the wavelengths is from 850 to 1048 nm (100 data poi...

Outliers have an important influence over the PCs, for this reason they must be detected and examinee.We have just the spectra without lab data, and we have to check if any of the sample spectra is an outlier ( a noisy spectrum, a sample which belongs ...

We saw how to plot the raw spectra (X), how to calculate the mean spectrum, how to center the sprectra (subtracting the mean spectrum from every spectra of the original matrix X). After that we have developed the PCAs with the NIPALS algorithm, getting...

This plot in 2D, help us to decide the number of PCs, it is easy to create in R, once we have discompose the X matrix into a P matrix (loadings) and a T matrix (scores).For this plot, we just need the T matrix.__ CPs  matp...

__ X X_nipals wavelengths matplot(w...

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 t...

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...

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...

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 ...

__ 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 ...

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...

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 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 summary(gas1)Data:   X ...

"R" has a package called "ChemometricsWithR", where we can get data from different analytical instruments including Near Infrared (NIR).Follow the steps to plot the spectra of a gasoline data set:In this other case we plot the spectra of the NIR shootout 2002: __ data(shootout)__ wavelengths mattplot(wavelengths,shootout$calibrate.1[1,],...

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(...

We started some posts based on the tutorials of:"Multivariate Statistical Analysis using the R package chemometrics"The first post was:Principal Components Analysis with "R" (Part: 001)Now we continue with a second part.The graphics help us to dec...

Centramos la matriz con el comando, generando a partir de A una nueva matriz que llamamos "Acentered"Acentered=scale(A,center=T)Ahora con la función "eigen":Esta es otra forma de proceder con el cálculo de los componentes principales (eigenvectors y ...