R version 3.1.0 (2014-04-10) -- "Spring Dance" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## > ## Domain means can be written as ratio estimators or as regression coefficients > ## > ## This code checks that subsetting the design object gives the same results as > ## these approaches. > ## > > > library(survey) Attaching package: 'survey' The following object is masked from 'package:graphics': dotchart > data(fpc) > dfpc<-svydesign(id=~psuid,strat=~stratid,weight=~weight,data=fpc,nest=TRUE) > dsub<-subset(dfpc,x>4) > (m1<-svymean(~x,design=dsub)) mean SE x 6.195 0.7555 > > ## These should give the same domain estimates and standard errors > (m2<-svyby(~x,~I(x>4),design=dfpc, svymean,keep.var=TRUE)) I(x > 4) x se FALSE FALSE 3.314286 0.3117042 TRUE TRUE 6.195000 0.7555129 > m3<-svyglm(x~I(x>4)+0,design=dfpc) > summary(m3) Call: svyglm(formula = x ~ I(x > 4) + 0, design = dfpc) Survey design: svydesign(id = ~psuid, strat = ~stratid, weight = ~weight, data = fpc, nest = TRUE) Coefficients: Estimate Std. Error t value Pr(>|t|) I(x > 4)FALSE 3.3143 0.3117 10.63 0.000127 *** I(x > 4)TRUE 6.1950 0.7555 8.20 0.000439 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 2.557379) Number of Fisher Scoring iterations: 2 > (m4<-svyratio(~I(x*(x>4)),~as.numeric(x>4), dfpc)) Ratio estimator: svyratio.survey.design2(~I(x * (x > 4)), ~as.numeric(x > 4), dfpc) Ratios= as.numeric(x > 4) I(x * (x > 4)) 6.195 SEs= as.numeric(x > 4) I(x * (x > 4)) 0.7555129 > stopifnot(isTRUE(all.equal(SE(m2), as.vector(SE(m3))))) > stopifnot(isTRUE(all.equal(SE(m2)[2], as.vector(SE(m4))))) > > ## with strata > data(api) > dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) > m1<-svymean(~enroll, subset(dstrat, comp.imp=="Yes")) > m2<-svyglm(enroll~comp.imp-1, dstrat) > m3<- svyratio(~I(enroll*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dstrat) > stopifnot(isTRUE(all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) > stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) > > ## with calibration > dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018) > (dclus1g3 <- calibrate(dclus1, ~stype+api99, c(pop.totals, api99=3914069))) 1 - level Cluster Sampling design With (15) clusters. calibrate(dclus1, ~stype + api99, c(pop.totals, api99 = 3914069)) > > m1<-svymean(~api00, subset(dclus1g3, comp.imp=="Yes")) > m3<-svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dclus1g3) > m2<-svyglm(api00~comp.imp-1, dclus1g3) > stopifnot(isTRUE( all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) > stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) > > ## with raking > pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018)) > pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122)) > dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide)) > m1<-svymean(~api00, subset(dclus1r, comp.imp=="Yes")) > m2<-svyglm(api00~comp.imp-1, dclus1r) > m3<-svyratio(~I(api00*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dclus1r) > stopifnot(isTRUE( all.equal(as.vector(SE(m2)["comp.impYes"]), as.vector(SE(m1))))) > stopifnot(isTRUE( all.equal(as.vector(SE(m1)), as.vector(drop(SE(m3)))))) > > > > ## > ## based on bug report from Takahiro Tsuchiya for version 3.4 > ## > rei<-read.table(tmp<-textConnection( + " id N n.a h n.ah n.h sub y + 1 1 300 20 1 12 5 TRUE 1 + 2 2 300 20 1 12 5 TRUE 2 + 3 3 300 20 1 12 5 TRUE 3 + 4 4 300 20 1 12 5 TRUE 4 + 5 5 300 20 1 12 5 TRUE 5 + 6 6 300 20 1 12 5 FALSE NA + 7 7 300 20 1 12 5 FALSE NA + 8 8 300 20 1 12 5 FALSE NA + 9 9 300 20 1 12 5 FALSE NA + 10 10 300 20 1 12 5 FALSE NA + 11 11 300 20 1 12 5 FALSE NA + 12 12 300 20 1 12 5 FALSE NA + 13 13 300 20 2 8 3 TRUE 6 + 14 14 300 20 2 8 3 TRUE 7 + 15 15 300 20 2 8 3 TRUE 8 + 16 16 300 20 2 8 3 FALSE NA + 17 17 300 20 2 8 3 FALSE NA + 18 18 300 20 2 8 3 FALSE NA + 19 19 300 20 2 8 3 FALSE NA + 20 20 300 20 2 8 3 FALSE NA + "), header=TRUE) > close(tmp) > > > des.rei2 <- twophase(id=list(~id,~id), strata=list(NULL,~h), + fpc=list(~N,NULL), subset=~sub, data=rei, method="full") > tot2<- svytotal(~y, subset(des.rei2, y>3)) > > rei$y<-rei$y*(rei$y>3) > ## based on Sarndal et al (9.4.14) > rei$w.ah <- rei$n.ah / rei$n.a > a.rei <- aggregate(rei, by=list(rei$h), mean, na.rm=TRUE) > a.rei$S.ysh <- tapply(rei$y, rei$h, var, na.rm=TRUE) > a.rei$y.u <- sum(a.rei$w.ah * a.rei$y) > V <- with(a.rei, sum(N * (N-1) * ((n.ah-1)/(n.a-1) - (n.h-1)/(N-1)) * w.ah * S.ysh / n.h)) > V <- V + with(a.rei, sum(N * (N-n.a) * w.ah * (y - y.u)^2 / (n.a-1))) > > a.rei$f.h<-with(a.rei, n.h/n.ah) > Vphase2<-with(a.rei, sum(N*N*w.ah^2* ((1-f.h)/n.h) *S.ysh)) > > a.rei$f<-with(a.rei, n.a/N) > a.rei$delta.h<-with(a.rei, (1/n.h)*(n.a-n.ah)/(n.a-1)) > Vphase1<-with(a.rei, sum(N*N*((1-f)/n.a)*( w.ah*(1-delta.h)*S.ysh+ ((n.a)/(n.a-1))*w.ah*(y-y.u)^2))) > > V [1] 70761.47 > Vphase1 [1] 44325.47 > Vphase2 [1] 26436 > vcov(tot2) [,1] [1,] 70761.47 attr(,"phases") attr(,"phases")$phase1 [,1] [1,] 44325.47 attr(,"phases")$phase2 [,1] [1,] 26436 > > ## comparing to regression > reg<-svyglm(y~I(y<4), design=des.rei2) > mn<-svymean(~y, subset(des.rei2,y>3)) > all.equal(as.vector(coef(reg))[1],as.vector(coef(mn))) [1] TRUE > all.equal(as.vector(SE(reg))[1],as.vector(SE(mn))) [1] TRUE > vcov(mn) [,1] [1,] 0.3292258 attr(,"phases") attr(,"phases")$phase1 [,1] [1,] 0.1599264 attr(,"phases")$phase2 [,1] [1,] 0.1692994 > vcov(reg) (Intercept) I(y < 4)TRUE (Intercept) 0.3292258 -0.3292258 I(y < 4)TRUE -0.3292258 0.5901907 attr(,"phases") attr(,"phases")$phase1 (Intercept) I(y < 4)TRUE (Intercept) 0.1599264 -0.1599264 I(y < 4)TRUE -0.1599264 0.2588542 attr(,"phases")$phase2 (Intercept) I(y < 4)TRUE (Intercept) 0.1692994 -0.1692994 I(y < 4)TRUE -0.1692994 0.3313365 > > > proc.time() user system elapsed 1.707 0.055 1.778