(This article was first published on

**NIR-Quimiometría**, and kindly contributed to R-bloggers)**> X<-yarn$NIR**

**> X_nipals<-nipals(X,a=10,it=100)**

Two matrices are generated (

**P**and**T**)As in other posts, we are going to look to the loadings & scores, for firsts three principal components:

**> wavelengths<-seq(1,268,by=1)**

**> matplot(wavelengths,X_nipals$P[,1:3],lty=1,**

**+ pch=21,xlab=”data_points”,ylab=”log(1/R)”)**

**> T3cp<-X_nipals$T[,1:3]**

**> pairs(T3cp)**

In the following plot, I compare the loadings plots for the first 3 PCs calculated with SVD (up) and with NIPALS (down):

We can see how the first PC (

**PC1**) has the same shape for both. The other two (**PC2**&**PC3**) has also the same shape, but inverted).Let´s compare the scores plots:

Red dots for NIPALS, black for SVD:

To

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