Is there a way to have the LDA function give me the best 3 (or 4) predictor
variables. When I put in all the variables, LDA uses all the variables, but I
would like to know what would be the 3 (or 4) best to use out all the available
variables and the coefficients for those.
Here is the code I am using for Linear Discriminant Function
library("MASS")
results <- lda(data$V1 ~ data$V2 + data$V3 + data$V4 + data$V5 + data$V6 +
data$V7 + data$V8 + data$V9 + data$V10 + data$V11 + data$V12 + data$V13 +
data$V14)
Output:
Coefficients of linear discriminants:
LD1 LD2
data$V2 -0.403399781 0.8717930699
data$V3 0.165254596 0.3053797325
data$V4 -0.369075256 2.3458497486
data$V5 0.154797889 -0.1463807654
data$V6 -0.002163496 -0.0004627565
data$V7 0.618052068 -0.0322128171
data$V8 -1.661191235 -0.4919980543
data$V9 -1.495818440 -1.6309537953
data$V10 0.134092628 -0.3070875776
data$V11 0.355055710 0.2532306865
data$V12 -0.818036073 -1.5156344987
data$V13 -1.157559376 0.0511839665
data$V14 -0.002691206 0.0028529846
So in the above example, I would like the LDA to return to me the 3 best
predictors out of the 13 available.
Thank you
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