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?
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