In October, someone posted the following question:
However, recently I read a Thomas Lumley's paper
("Analysing Survey Data
in
R", R News 3(1), 2003) in which he states that:
"Estimates on a subset of a survey require some care. Firstly, it is
important to ensure that the correct weight, cluster and stratum
information
is kept matched to each observation. Secondly, it is
not correct simply to
analyse a subset as if it were a designed survey of its own. The correct
analysis corresponds approximately to setting the weight to zero for
observations not in the subset. This is not how it is implemented, since
observations with zero weight still contribute to constraints in R
functions
such as glm, and they still take up memory. Instead,
the survey.design
object keeps track of how many clusters (PSUs) were originally present in
each stratum and svyCprod uses this to adjust the variance."
According to this statement, subset analysis (for instance, comparing
blacks
and whites, men and women, catholic and protestant,
and so on) would be
possible since we keep all cases and survey design information while
running
our analysis, just attributing zero weight to cases
outside the subset of
interest.
My question would be whether or not there is a Zelig command that
automatize
this 'zero weighting process'. (but, of
course, it is possible that my
question in fact does not make sense.)
And response:
Dear Fabricio,
No, Zelig does not automate this. However, if you follow Thomas'
suggestion in the survey design object input to Zelig, Zelig should be
able to accommodate this since it passes all the *.survey models to
Thomas' package.
Best,
Olivia
I tried to follow this advice but without success. Does anyone know how to
get zelig to run on a subset from a complex survey design data file?
Respectfully,
Frank Lawrence
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