# Linear discriminant analysis in R: how to choose the most suitable model?

The data set vaso in the robustbase library summarizes the vasoconstriction (or not) of subjects’ fingers along with their breathing volumes and rates.

> head(vaso)
Volume  Rate Y
1   3.70 0.825 1
2   3.50 1.090 1
3   1.25 2.500 1
4   0.75 1.500 1
5   0.80 3.200 1
6   0.70 3.500 1


I want to perform a linear discriminant analysis in R to see how well these distinguish between the two groups. And I consider two cases:

ld <- lda(Y ~ ., data=vaso)

ld1 <- lda(Y ~ log(Volume)+log(Rate), data=vaso)


Please help me understand which model is better? What characteristics to look at?

• Your question is not very clear to me, but maybe it's because I'm not familiar with LDA. I understand you only want to measure the impact of the features on Y, right? I'm not sure it's possible to say which variant is "better" if you don't evaluate anything. Also in the second variant you're testing the log of both features, any particular reason for that? Jun 24 '20 at 16:13
• @Erwan Yes, I really want to measure the impact of the features on Y, but I want to do it in the most appropriate way. And for this I want to choose the most suitable model. Jun 24 '20 at 16:21