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Develop documentation about weights. Add docstrings for `vcov` and `stderror`. Move implementation and comparison with R to separate page and mention weights. Show `using` lines in examples instead of running them in a hidden block so that users can reproduce them. Also improve various details.
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nalimilan
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Jan 3, 2026
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| When the model is weighted with `ProbabilityWeights`, the sandwich estimator is used, | ||
| scaling by `nobs(x)/(nobs(x) - 1)` degrees of freedom. |
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@gragusa It's not clear to me what justifies these degrees of freedom, nor why Stata uses a different approach. Would you have references about this in R's survey and Stata, and/or would you suggest wording improvements? These would be useful for users.
Am I right that this is completely different from the df.resid argument that can be passed to summary.svyglm?
nalimilan
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Jan 3, 2026
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| Using analytic weights corresponds to weighted least squares. | ||
| This gives the same results as R and Stata. | ||
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| Probability weights give the same point estimates as analytic weights, but standard errors | ||
| and p-values are based on a sandwich (heteroskedasticity-robust) estimator. | ||
| This gives the same results as the R `survey` package with a simple survey design | ||
| without strata nor clustering, but differs from Stata with the `pweights` option, which | ||
| adopts the same approach but with a different assumption regarding degrees of freedom. |
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@gragusa This paragraph is worth checking too.
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Develop documentation about weights. Add docstrings for
vcovandstderror. Move implementation and comparison with R to separate page and mention weights. Showusinglines in examples instead of running them in a hidden block so that users can reproduce them. Also improve various details.