[R] analysis of data with observation weights

[John Fox]

Dear Michal,

As far as I know (and I'd be happy to be wrong), there's no *general* way 
of introducing case weights in R. The glm function, however, accommodates 
case weights via its weights argument, and this might be sufficient to do 
what you want to do. You'll have to be careful with inferences, though.

Perhaps someone else on the list can provide additional information.

John

At 05:22 PM 11/14/2002 +0100, Michal Bojanowski wrote:

>Recently I had to analyze a dataset from household survey. The sample design
>ensured, that each household in the population has the same probability of 
>being
>sampled. However the data were gathered from only one adult individual in each
>household, who was randomly choosen by an interviewer (via "Kish grid"). To
>equalize the probabilities for each INDIVIDUAL a casewise weighting factor is
>introduced. It is proportional to the reciprocal of the number of adults 
>in the
>household and rescaled so it's sum equals the sample size. This weighting 
>factor
>is neccessery to perform inferences for population of individuals.
>
>I had no problems with estimating models which use count data, because I could
>construct contingency tables with something like:
>
>tapply(weight, a.bunch.of.factors, sum)
>
>Unfortunately I couldn't come up with a good way of building other kinds of
>models for those data. Is there some way (apart for writing new functions from
>scratch) to perform modelling tasks like lm(), that will take the weights into
>account?
>
>(As far as I know there are only basic functions weighted.mean() and cov.wt()
>for weighted means and weighted covariance/correlation matrices respectively.)

-----------------------------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario, Canada L8S 4M4
email: jfox at mcmaster.ca
phone: 905-525-9140x23604
web: www.socsci.mcmaster.ca/jfox
-----------------------------------------------------

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