Here's one way to do it ...

# create example data frame
y <- rnorm(30)
gene_subset <- data.frame(y, x1=rnorm(30), x2=rnorm(30), x3=100*y+rnorm(30))

# fit a full linear model
fit <- lm(y ~ ., df)

# reduce the model
reduced_model <- stepAIC(fit, trace=FALSE)

# NON-omitted variables (excluding the response)
keepx <- names(reduced_model$model)[-1]
index <- match(keepx, names(gene_subset))

Jean



On Mon, Aug 26, 2013 at 12:51 AM, Sachinthaka Abeywardana <
sachin.abeywardana@gmail.com> wrote:

> I am regressing a gene on another gene subset. Then I use stepAIC to reduce
> the number of explanatory genes. How do I get the index of the NON-omitted
> variables, so that I could analyse them?
>
>     gene_subset=c(y=genes[,i], genes[,other_genes]);
>     reduced_model=stepAIC(y~.,data=gene_subset,trace=false);
>
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>
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