In this example we use the `dclus2` two-stage cluster sample from the California Academic Performance Index, which was created in an earlier example. The syntax and options for `svyglm` are the same for designs with and without replicate weights.

The outcome variable is 2000 API, predicted by the proportions of students learning English (`ell`), receiving subsidized means (`means`) and having moved to the school within the past year (`mobility`). This is a linear regression model, so no `family` argument to `svyglm` is needed.

> summary(svyglm(api00 ~ ell + meals + mobility, design = dclus2)) Call: svyglm.survey.design(formula = api00 ~ ell + meals + mobility, design = dclus2) Survey design: svydesign(id = ~dnum + snum, weights = ~pw, data = apiclus2) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 811.4907 30.8795 26.279 <2e-16 *** ell -2.0592 1.4075 -1.463 0.146 meals -1.7772 1.1053 -1.608 0.110 mobility 0.3253 0.5305 0.613 0.541 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 8296.727) Number of Fisher Scoring iterations: 2A useful property of regression models is that they provide another way to get domain estimates. Suppose we want the mean of

> summary(svyglm(api00~stype-1, dclus2)) Call: svyglm.survey.design(formula = api00 ~ stype - 1, design = dclus2) Survey design: svydesign(id = ~dnum + snum, weights = ~pw, data = apiclus2) Coefficients: Estimate Std. Error t value Pr(>|t|) stypeE 692.81 30.28 22.88 <2e-16 *** stypeH 598.34 16.96 35.27 <2e-16 *** stypeM 642.35 45.34 14.17 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 17389.33) Number of Fisher Scoring iterations: 2 > svyby(~api00,~stype,dclus2,svymean, keep.var=TRUE) stype statistic.api00 SE E E 692.8104 30.28244 H H 598.3407 16.96500 M M 642.3520 45.34363This equivalence helps in thinking about domain estimators and how they handle more complex designs.

Thomas Lumley Last modified: Mon Jun 13 15:43:36 PDT 2005