Mosaic 7.12 is now available on our Europe server

Mosaic version 7.12 is now available on our Europe server.

Once you try to connect, you should be asked to automatically download and install the new client.
User accounts, passwords remain the same.

Ports used for NOVA:

 

Configure correctly the ports with your IT for a successful synchonization.

Easy way to check the eigen values with the T (scores) matrix

Other interesting Matrix multiplication is the product of the score matrix T by it´s transpose in this way:

Tt%*%T

This product give us a square matrix (a.a), being “a” the number of loadings or PCs chosen, and the diagonal has the eigenvalues which are related to the quantity of explained variance for every loading.

If we plot the diagonal we can see how the eigenvalue decrease by every loading. This plot can help us to decide how many loadings or PCs to choose.



Add caption

 

Checking the orthogonality of P (loadings) matrix

One of the values we got in the script of the post:"Tutorials with Resemble (Part 3 - orthoProjection) " was the loadings matrix (X.loadings), or what we called usually in this blog the P matrix.

One of the characteristics of the loadings “P” matrix, when we develop the PCA, is that if we multiply it by its transpose we get the Identity Matrix “I”

P<-X.loadings

Pt<-t(X.loadings)

 

P%*%Pt = I

 

In the “I” matrix, its diagonal is “1”, and “0” values for all the rest cells indicating that all the loadings are orthogonal between them.

Exercise:

• Check it by yourself and take out the diagonal from the P matrix.
• Represent in a graphic the first loadings:
• 1 vs 2      : a plane
• 1, 2 and 3: a cube

 

How to load a REP file in a MOSAIC LOCAL Prediction Model

If we use the MONITOR in Win ISI or a LOCAL Prediction Model in ISI Scan, there is a field to load the REP file (is a ".nir" which include the variation we want to minimize in the model, like the temperature, differences between instruments, differences between the pathlengths of the gold reflectors,….). This way the LOCAL uses the REP file when developing the calibration.

In MOSAIC the REP file must be load in a different way.

As usual we load the ".RED" file, reduced with the appropriate math-treatment, we set the maximum and minimum number of factors and samples,...., but where I load the repeatability file (.NIR) .

😏...Easy but tricky.

Rename the extension from the repeatability file from ".NIR" to ".REP", and give to this file the same name than the ".RED" file; put them both in the same folder. Now when you import the ".RED" file to the LOCAL Prediction Model, the ".REP" file will go with it. Just check it on the Links tab of the LOCAL P.M.

 

As you know something similar happens when whe load a ".EQA" and load also the ".PCA" and ".LIB" files

Thanks to Montse for testing this feature...😉

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!