R version 3.1.0 (2014-04-10) -- "Spring Dance"
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> ##
> ## 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
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