## 16 abr. 2018

### Correlation Matrix plot

In the last posts we are talking about the wavelength space due to its high collinearity, because we want to select wavelengths with few correlation between them in order to develop a model.

In this task we can check the correlation matrix, which is better to check in a plot than with numbers. This is the plot for the soy meal samples in transmitance using the 100 wavelengths from 850 to 1048 nm in steps of 2 nm, so the correlation matrix is a 100.100 diagonal and symmetric matrix as can be seen in the plot.

The red line is the correlation of the 962 nm wavelength with all the rest including itself (1 in this case). The vertical blue lines are the wavelengths at 1022,902 and 962 used in the recent posts.

See the Correlation matrix plot and code:
cor_Xcmsc<-cor(X_msc_centered)
matplot(wavelengths,t(cor_Xcmsc),type="l",

xlab="wavelengths",
ylab="Correlation",

col="grey",ylim=c(-1.00,1.00))
par(new=TRUE)
matplot(wavelengths,cor_Xcmsc[58,],type="l",

xlab="wavelengths",
ylab="Correlation",

col="red",ylim=c(-1.00,1.00))
abline(v=1022,col="blue")
abline(v=902,col="blue")
abline(v=964,col="blue")