Hello R list,

I'm looking to do some stepwise discriminant function analysis (DFA) based
on the minimization of Wilks' lambda in R to end up with a composite
signature (of metals "Al","Sb","Bi","Cr","Ba") capable of discriminating
100% of the source factors (LANDUSE: "A","B","C").

The Wilks' lambda portion seems straightforward. I am using the following:

gw_obj <- greedy.wilks(LANDUSE ~ ., data = QRBdfa, niveau = 0.1)
gw_obj

Thus determining the stepwise order of metals.But I can't seem to figure out
how to coerce the DFA to give me an output with the % of factors which each
successive metal (variable) correctly classifies (discriminates). e.g.

Step    Metal        %correctly classified
1            Al                25
2            Sb               75
3            Bi                89
4            Cr               100

I've worked up a trivial example below. Can anyone offer any suggestions on
how I might go about doing this in R?

I am working in a MAC OS environment with a current version of R.

Many thanks in advance!

Tyler

#Example
library(scatterplot3d)
library(klaR)

Al <-runif(27, 0, 125)
QRBdfa <- as.data.frame(Al)
QRBdfa$LANDUSE <- factor(c("A","A","A","B","B","B","C","C","C"))
QRBdfa$Sb <- runif(27, 0, 1)
QRBdfa$Ba <- runif(27, 0, 235)
QRBdfa$Bi <- runif(27, 0, 0.11)
QRBdfa$Cr <- runif(27, 0, 65)


gw_obj <- greedy.wilks(LANDUSE ~ ., data = QRBdfa, niveau = 0.1)
gw_obj


fit <- lda(LANDUSE ~ Al + Sb + Bi + Cr + Ba, data = QRBdfa)

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