Thanks for the answers.
Dear Marco and Goran,
Perhaps the documentation could be clearer, but it is after all a brief help
page. Using weights of 2 to lm() is *not* equivalent to entering the
observation twice. The weights are variance weights, not case weights.
According to your post here:
http://tolstoy.newcastle.edu.au/R/e2/help/07/05/16311.html
there are 3 possible kinds of weights.
The person in this one:
http://tolstoy.newcastle.edu.au/R/e2/help/07/06/18743.html
includes 2 others making a distinction between weights inverse
proportional to variance and weight equal to inverse variance.
(looking at other posts in the thread shows that other people also make
confusions on this matter)
So R's lm(), glm(), etc weights **are** the inverse of the variance of
the observations, right?
They'are not **proportional** to the inverse of variance because if this
were true, then weight and 2*weight would archive the same results, right?
I needed a method to use proportional weights on observations as I know
their proportion of variance among each other.
And it doesn't need to be a R function, just an explanation on how
construct the likehood would be fine. If anybody know an article on the
subject, would be of great help to.
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