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)
[1] "scores" "X.loadings" "variance" "sc.sdv" "n.components"
[6] "pcSelection" "center" "scale" "method"
 

 >attach(oP)
>scores
Matrix T of scores
>X.loadings
Matrix P of Loadings
>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)
3.3.......Plot the loadings.                                 (page Exercises)
3.4.......Plot combinations of score Maps            (page Exercises)

¡And enjoy Chemometrics with R!

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