[This article was first published on NIR-Quimiometría, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
We have seen in the previous post, how to calculate the correlation spectrum, but other simple way to show how the bands correlate to the constituent of interest is to calculate R^2. This way we remove the negative part of the correlation spectrum.
Xmsc<-NIR_msc
Ymoi<-demo_raw$Moisture
cor_spec<-cor(Ymoi,Xmsc[,1:700])
rsq_spec<-(cor(Ymoi,Xmsc[,1:700]))^2
cov_spec<-cov(Ymoi,Xmsc[,1:700])*50
matplot(wave_nir,t(cor_spec),lty=1,pch=”*”,xlab=”nm”,ylab=”log(1/R)”)
matplot(wave_nir,t(rsq_spec),lty=1,pch=”*”,xlab=”nm”,ylab=”log(1/R)”)
matplot(wave_nir,t(cov_spec),lty=1,pch=”*”,xlab=”nm”,ylab=”log(1/R)”)
#We merge the R /R^2/Cov spectrum with the sample spectra treated with MSC.
cor_spec<-rbind(cor_spec,NIR_msc)
rsq_spec<-rbind(rsq_spec,NIR_msc)
cov_spec<-rbind(cov_spec,NIR_msc)
matplot(wave_nir,t(cor_spec),lty=1,pch=”*”,xlab=”nm”,ylab=”log(1/R)”)
matplot(wave_nir,t(rsq_spec),lty=1,pch=”*”,xlab=”nm”,ylab=”log(1/R)”)
matplot(wave_nir,t(cov_spec),lty=1,pch=”*”,xlab=”nm”,ylab=”log(1/R)”)
In order to see better the Covariance Spectrum, I multiplied by a factor,We can see how the covariance spectrum gives sharp bands an gives us a better idea where the variation due to moisture is.
To leave a comment for the author, please follow the link and comment on their blog: NIR-Quimiometría.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.