data(api)
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2)
rstrat<-as.svrepdesign(dstrat)
rclus2<-as.svrepdesign(dclus2)
summary(svyglm(api00~ell+meals+mobility, design=dstrat))
summary(svyglm(api00~ell+meals+mobility, design=dclus2))
summary(svyglm(api00~ell+meals+mobility, design=rstrat))
summary(svyglm(api00~ell+meals+mobility, design=rclus2))
## use quasibinomial, quasipoisson to avoid warning messages
summary(svyglm(sch.wide~ell+meals+mobility, design=dstrat,
family=quasibinomial()))
## Compare regression and ratio estimation of totals
api.ratio <- svyratio(~api.stu,~enroll, design=dstrat)
pop<-data.frame(enroll=sum(apipop$enroll, na.rm=TRUE))
npop <- nrow(apipop)
predict(api.ratio, pop$enroll)
## regression estimator is less efficient
api.reg <- svyglm(api.stu~enroll, design=dstrat)
predict(api.reg, newdata=pop, total=npop)
## same as calibration estimator
svytotal(~api.stu, calibrate(dstrat, ~enroll, pop=c(npop, pop$enroll)))
## svyglm can also reproduce the ratio estimator
api.reg2 <- svyglm(api.stu~enroll-1, design=dstrat,
family=quasi(link="identity",var="mu"))
predict(api.reg2, newdata=pop, total=npop)
## higher efficiency by modelling variance better
api.reg3 <- svyglm(api.stu~enroll-1, design=dstrat,
family=quasi(link="identity",var="mu^3"))
predict(api.reg3, newdata=pop, total=npop)
## true value
sum(apipop$api.stu)
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