PLS-DA: prediction of new upcoming samples

Support
1

Hi there,

I have a practical question regarding PLS-DA/sPLS-DA.

Let’s say I have created a PLS-DA model based on some samples, and want to use it to classify new unlabelled samples. I could not find the way of doing this, is that a possibility?

Thanks

2

Explore the predict() function. If you’d like some usage examples, look here

3

@MaxBladen perfect! That’s what I am looking for, thanks.

4

One additional question from the tutorial you have linked.
It is about splitting the dataset for training and testing.

I have in total 184 samples. Did the split as instructed in the tutorial, where for training I have allocated 140 samples, and the rest is testing set (44 samples)

train ← sample(1:nrow(X), 140) # randomly select 140 samples in training
test ← setdiff(1:nrow(X), train)

store matrices into training and test set:

X.train ← X[train, ]
X.test ← X[test,]
Y.train ← Y[train]
Y.test ← Y[test]

After training

train.splsda.h2s ← splsda(X.train, Y.train, ncomp = optimal.ncomp, keepX = optimal.keepX)

and testing

predict.splsda.h2s ← predict(train.splsda.h2s, X.test, dist = “mahalanobis.dist”)

evaluation with the confusion matrix shows only 6 samples.

predict.comp2 ← predict.splsda.h2s$class$mahalanobis.dist[,2]
table(factor(predict.comp2, levels = levels(Y)), Y.test)

I would expect here evaluation of all 44 test samples. Why is that?

5

So the confusion matrix only sums to 6? Are you sure nrow(X) returns 184?

My best guess is that some of the values in predict.comp2 are NAs. If not, then I don’t know what would be causing that.

Let me know if you cant resolve it and I’ll look into it.