[R] Residual variance from rlm?

From: Talbot Katz <topkatz_at_msn.com>
Date: Fri 26 Jan 2007 - 19:21:06 GMT


Hi.

This is a real basic question about results from rlm. I want to compute the properly scaled residual variance.

Suppose M is my rlm result object; my example regression is against two variables, and based on 225 observations. summary(M) tells me that
"Residual standard error: 0.0009401 on 222 degrees of freedom" which I presume is the same result as

summary(M)$sigma:	0.0009401223
Then, summary(M)$sigma^2:	8.8383e-07

Is the value of summary(M)$sigma^2 the proper residual variance? If so, I'd like to be able to replicate that from M$wresid and M$w, but I haven't been able to. For example,

var(M$wresid*M$w) = sum((M$wresid*M$w)^2)/224		6.350269e-07
mean(M$wresid^2*M$w) = sum(M$wresid^2*M$w)/225		9.45235e-07
Note that sum(M$w)		205.8032
I was disappointed to find that M$df.residual was NA; however, summary(M)$df 
does return a vector:	3 222   3

I have tried a bunch of other combinations of M$wresid and M$w, but nothing I've tried comes out the same as summary(M)$sigma^2.

Again, is summary(M)$sigma^2 the proper residual variance? If yes, can it be replicated from the M object? If no, can I compute the proper value from the M object?

Thanks!


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