I am trying to analyse a dataset with at minimum 50 explanatory variables coded as 0 and 1 for presence/absence and a binary response variable (case/control). The goal is to see how the variables can predict the separation between case and control.
As there are more variables than observations I applied a partial least square discriminant analysis (PLS-DA) using the package mixOmics in R. However, when I want to test the significance of the analysis with PLSDA.test (package RVAideMemoire) I get a lot of warnings :
1: In pls(X, ind.mat, ncomp = ncomp, mode = "regression", ... : Zero- or near-zero variance predictors. Reset predictors matrix to not near-zero variance predictors. See $nzv for problematic predictors.
I guess the problem with near-zero variance results from the 0/1 coding of the predictor variables. I tried to convert the variables to factors, but this doesn't help. Is there a different analysis more suitable? How can I deal with presence/ absence variables as predictors?