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2007 Jun 11
0
Weighted least squares
...errors, with observations that have low-variance errors
>therefore being accorded greater weight in the resulting WLS regression.
>What you have are sometimes called "case weights," and I'm unaware of a
>general way of handling them in R, although you could regenerate the
>unaggregated data. As you discovered, you get the same coefficients with
>case weights as with variance weights, but different standard errors.
>Finally, there are "sampling weights," which are inversely proportional to
>the probability of selection; these are accommodated by the survey p...
2007 May 08
5
Weighted least squares
Dear all,
I'm struggling with weighted least squares, where something that I had
assumed to be true appears not to be the case. Take the following
data set as an example:
df <- data.frame(x = runif(100, 0, 100))
df$y <- df$x + 1 + rnorm(100, sd=15)
I had expected that:
summary(lm(y ~ x, data=df, weights=rep(2, 100)))
summary(lm(y ~ x, data=rbind(df,df)))
would be equivalent, but