
Dear R fans,
I have got a difficult sounding problem.

For fitting a linear model using continuous response and then for re-fitting the model after excluding every single variable, the following functions can be used.
library(MASS)
model = lm(perf ~ syct + mmin + mmax + cach + chmin + chmax, data = cpus) 
dropterm(model, test = "F")

But I am not sure whether any similar functions is available in R for multivariate data with categorical response. 
My data looks like the following: 
mat <- matrix(rnorm(700), ncol=5, dimnames=list( paste("f", c(1:140), sep="_"), c("A", "B", "C", "D", "E")))

There are 140 features describing 5 different plant species. I want to retain only those features that show good performance in model (by using a function similar to dropterm, which can not be used for mlm objects).
 
I wud appreciate some hints n suggestions.

Thx
- Vickie




 		 	   		  
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