Finally happy with this plot trying to explain the precision and accuracy of the laboratory vs. the NIR predictions for a set of samples and subsamples. I will explain more detail of this plots in coming posts.
14 jun. 2018
6 jun. 2018
This is a boxplot where there are four subsamples of meat meal predictions. A representative of a certain batch has been divided in four subsamples and analyzed in a NIR. So we get four predictions, one for every subsamples, so the the boxplot gives an idea of the variance in the predictions for every sample based on their subsamples.
The colors are because the subsamples had been send to two different labs, so one is represented by one color. Colors had certain transparency because in some cases, two samples went to a lab and two to the other, in other cases the four subsamples went to the same lab and even in some cases three to one lab and one to another.
All these studies give an idea of the complexity of the meat meal product.
3 jun. 2018
In order to understand better the performance of a model, different blind subsamples of a sample had been sent to a laboratory, so in some cases we have the lab values of four subsamples of a sample and in other cases two subsamples of a sample. There are two cases with only one subsample.
For every subsample we calculate the average for the lab, and the average for the predictions, to get the red dot residuals.
We have also the residual of every subsample vs its prediction and those are the gray dots.
The plot (with R) gives a nice information about the performance of the model and how the average performs better in most cases than the individual subsamples.
We can see the warning (2.SEL) and action limits (3.SEL), and how the predictions for the average fall into the warning limits.