This practical work is based on an initial document written by Boris Hejblum

Load the mixOmics package and liver.toxicity data:

library(mixOmics)
data("liver.toxicity")

As before, we want to predict the albumine level using gene expression data.

  1. A first PLS model
    Consult the pls() help page, and fit a PLS regression model, first using \(r=10\) (10 latent variables).

  2. Number of latent variables
    How many latent variables do we have to keep ?
    [Use the perf() function]

  3. PLS regression characteristics
    Fit the PLS regression model with the previously chosen number of latent variables.
    What are the explained variance proportions (see the results of the summary() function on the PLS object).

  4. Individuals representation
    Plot the projection of indivuals on the first two latent variables of \(X\) (see plotIndiv() function), and add the measurement time and the paracetamol dose (see liver.toxicity$treatment object). Comment.

  5. Variables representation
    Plot the links between \(X\) variables and the \(Y\) variable (see the plotVar()) function. Comment.

  6. Predictions
    Predict the learning set observations and compute the empirical error of the PLS predictor. Comment.