From p.dalgaard at biostat.ku.dk  Wed May 24 10:14:23 2006
From: p.dalgaard at biostat.ku.dk (Peter Dalgaard)
Date: 24 May 2006 10:14:23 +0200
Subject: [Rd] (PR#8877) predict.lm does not have a weights argument for
In-Reply-To: <20060524053520.1ABA5CD41@slim.kubism.ku.dk>
References: <20060524053520.1ABA5CD41@slim.kubism.ku.dk>
Message-ID: <x2k68bdelc.fsf@viggo.kubism.ku.dk>

ripley at stats.ox.ac.uk writes:

> (a) case weights:  w_i = 3 means `I have three observations like (y, x)'
> 
> (b) inverse-variance weights, most often an indication that w_i = 1/3 
> means that y_i is actually the average of 3 observations at x_i.
> 
> (c) multiple imputation, where a case with missing values in x is split 
> into say 5 parts, with case weights less than and summing to one.
> 
> (d) Heteroscedasticity, where the model is rather
> 
>          y = x\beta + \epsilon, \epsilon \sim N(0, \sigma^2(x))
> 
> And there may well be other scenarios, but those are the most common (in 
> decreasing order) in my experience.

I'd have (d) higher on the list, but never mind. There's also

(e) Inverse probability weights: Knowing that part of the population
is undersampled and wanting results that are compatible with what you
would have gotten in a balanced sample. Prototypically: You sample X,
taking only a third of those with X > c; find population mean of X,
(or univariate regression on some other variable, which is only
recorded in the subsample).

(R-bugs stripped from recipients since this doesn't really have
anything to do with the purported bug.)

       
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   O__  ---- Peter Dalgaard             ?ster Farimagsgade 5, Entr.B
  c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
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[Rd] (PR#8877) predict.lm does not have a weights argument for
Peter Dalgaard