24 mar. 2017

Tutorials with Resemble (Part 3 - orthoProjection)

Using orthoProjection:
One of the different functions of Resemble is “orthoProjection” and we can use it with different options. Let check in this post the simplest one:
oP<-orthoProjection(Xr=der.Xr, X2 = NULL,
Yu = NULL,method = "pca",
pcSelection = list("cumvar",0.99),
center = TRUE, scaled = FALSE,
cores = 1)
We can use the training data from the previous post, with the SG filter (just for smoothing) and the first derivative: der.Xr
The method we use is “pca”, so we don´t have to use the reference data “Yr”. We don´t use any additional set so X2=NULL
The number of terms will explain a cumulative variance of 99%.
We center the spectra, and we don´t scale it.
Now run this script in R (be sure that the package Resemble is loaded, library(resemble))

Now we can check the values we get:
names(oP)
 "pcSelection" "center" "scale" "method"

>attach(oP)
>scores
Matrix T of scores
>Variance
We can see the eigenvalue, the cumulative and explained variance
>sc.sdv
eigenvalues
>n.components
Number of terms chosen to explain 99% of the variance
>pcSelection
cumvar  0,99
>center
average spectrum
>scale
1
>method
pca(svd)

Check all these values and matrices.
3.1.......Practice plotting the average spectrum. (page Exercises)
3.2.......Play with the accumulative variance.     (page Exercises)