I am trying to run a Partial Least Squares Discriminant Analysis (PLSDA) in R, followed by a prediction and confusion matrix, using the mdatools package. The mdatools::plsda function works normally, as does stats::predict, but I have been experiencing difficulties with caret::confusionMatrix.

Here is a reproducible example:

library(tidyverse)
library(mdatools)
library(caret)

#GetData

data(iris)

#Sampling 50/50 for train and valid

set.seed(2000)
Train <- iris %>% group_by(Species) %>% sample_frac(.5, replace = FALSE)
Valid <- anti_join(iris, Train)

#Separating categorical variable from numerical variables

PLSDA_Response = as.matrix(Train[,5])
PLSDA_Predictors = as.matrix(Train[,c(1:4)])

Predict_Response = as.matrix(Valid[,5])
Predict_Predictors = as.matrix(Valid[,c(1:4)])

#Run PLS-DA

pls_comp <- mdatools::plsda(PLSDA_Predictors, as.factor(PLSDA_Response), ncomp = 1)

#Run the prediction

pls_comp_predict <- stats::predict(pls_comp, Predict_Predictors)

#Run the Confusion Matrix

pls_CM <- caret::confusionMatrix(pls_comp_predict, as.factor(Predict_Predictors))

The message I receive is: Error: `data` and `reference` should be factors with the same levels.

Can someone help me, please? Thanks! 🙂