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>
> ## examples from Breslow & Chatterjee: Applied Statistics 1999 No. 4, p458
> ## data from Norman Breslow's web page.
> library(survey)
Loading required package: grid
Loading required package: Matrix
Loading required package: survival
Attaching package: ‘survey’
The following object is masked from ‘package:graphics’:
dotchart
> load("nwts.rda")
> nwtsnb<-nwts
> nwtsnb$case<-nwts$case-nwtsb$case
> nwtsnb$control<-nwts$control-nwtsb$control
>
> a<-rbind(nwtsb,nwtsnb)
> a$in.ccs<-rep(c(TRUE,FALSE),each=16)
>
> b<-rbind(a,a)
> b$rel<-rep(c(1,0),each=32)
> b$n<-ifelse(b$rel,b$case,b$control)
>
> index<-rep(1:64,b$n)
>
> nwt.exp<-b[index,c(1:3,6,7)]
> nwt.exp$id<-1:4088
>
> dccs2<-twophase(id=list(~id,~id),subset=~in.ccs,
+ strata=list(NULL,~interaction(instit,rel)),data=nwt.exp)
>
> dccs8<-twophase(id=list(~id,~id),subset=~in.ccs,
+ strata=list(NULL,~interaction(instit,stage,rel)),data=nwt.exp)
>
> gccs8<-calibrate(dccs2,phase=2,formula=~interaction(instit,stage,rel))
>
> summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs2))
Call:
svyglm(formula = rel ~ factor(stage) * factor(histol), design = dccs2,
family = quasibinomial)
Survey design:
twophase2(id = id, strata = strata, probs = probs, fpc = fpc,
subset = subset, data = data)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.5701 0.1288 -19.955 < 2e-16 ***
factor(stage)2 0.5482 0.1979 2.769 0.005708 **
factor(stage)3 0.4791 0.2032 2.359 0.018515 *
factor(stage)4 1.0037 0.2592 3.872 0.000114 ***
factor(histol)2 1.3505 0.3107 4.346 1.51e-05 ***
factor(stage)2:factor(histol)2 0.1152 0.4410 0.261 0.793876
factor(stage)3:factor(histol)2 0.5066 0.4241 1.194 0.232548
factor(stage)4:factor(histol)2 0.9785 0.6214 1.575 0.115615
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 1.000876)
Number of Fisher Scoring iterations: 4
> summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs8))
Call:
svyglm(formula = rel ~ factor(stage) * factor(histol), design = dccs8,
family = quasibinomial)
Survey design:
twophase2(id = id, strata = strata, probs = probs, fpc = fpc,
subset = subset, data = data)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.71604 0.10827 -25.085 < 2e-16 ***
factor(stage)2 0.78141 0.14726 5.306 1.35e-07 ***
factor(stage)3 0.80093 0.15250 5.252 1.80e-07 ***
factor(stage)4 1.07293 0.17817 6.022 2.33e-09 ***
factor(histol)2 1.45836 0.31780 4.589 4.96e-06 ***
factor(stage)2:factor(histol)2 -0.04743 0.43495 -0.109 0.913
factor(stage)3:factor(histol)2 0.28064 0.41298 0.680 0.497
factor(stage)4:factor(histol)2 0.90983 0.63774 1.427 0.154
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 1.000876)
Number of Fisher Scoring iterations: 4
> summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=gccs8))
Call:
svyglm(formula = rel ~ factor(stage) * factor(histol), design = gccs8,
family = quasibinomial)
Survey design:
calibrate(dccs2, phase = 2, formula = ~interaction(instit, stage,
rel))
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.71604 0.10878 -24.968 < 2e-16 ***
factor(stage)2 0.78141 0.14729 5.305 1.35e-07 ***
factor(stage)3 0.80093 0.15212 5.265 1.68e-07 ***
factor(stage)4 1.07293 0.17905 5.993 2.77e-09 ***
factor(histol)2 1.45836 0.31757 4.592 4.88e-06 ***
factor(stage)2:factor(histol)2 -0.04743 0.43432 -0.109 0.913
factor(stage)3:factor(histol)2 0.28064 0.41231 0.681 0.496
factor(stage)4:factor(histol)2 0.90983 0.63187 1.440 0.150
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 1.000876)
Number of Fisher Scoring iterations: 4
>
> ## check subsets of calibrated designs.
> summary(svyglm(rel~factor(stage),
+ family=quasibinomial,design=subset(dccs8,histol==1)))
Call:
svyglm(formula = rel ~ factor(stage), design = subset(dccs8,
histol == 1), family = quasibinomial)
Survey design:
subset(dccs8, histol == 1)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.7160 0.1083 -25.085 < 2e-16 ***
factor(stage)2 0.7814 0.1473 5.306 1.48e-07 ***
factor(stage)3 0.8009 0.1525 5.252 1.97e-07 ***
factor(stage)4 1.0729 0.1782 6.022 2.73e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 1.001333)
Number of Fisher Scoring iterations: 4
Warning messages:
1: In `[.twophase2`(x, r, ) : 1 strata have only one PSU in this subset.
2: In summary.glm(g) :
observations with zero weight not used for calculating dispersion
3: In summary.glm(glm.object) :
observations with zero weight not used for calculating dispersion
4: In `[.twophase2`(design, nas == 0, ) :
1 strata have only one PSU in this subset.
5: In `[.twophase2`(design, nas == 0, ) :
1 strata have only one PSU in this subset.
> summary(svyglm(rel~factor(stage),
+ family=quasibinomial,design=subset(gccs8,histol==1)))
Call:
svyglm(formula = rel ~ factor(stage), design = subset(gccs8,
histol == 1), family = quasibinomial)
Survey design:
subset(gccs8, histol == 1)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.7160 0.1082 -25.105 < 2e-16 ***
factor(stage)2 0.7814 0.1457 5.363 1.10e-07 ***
factor(stage)3 0.8009 0.1504 5.324 1.34e-07 ***
factor(stage)4 1.0729 0.1759 6.101 1.70e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 1.001333)
Number of Fisher Scoring iterations: 4
Warning messages:
1: In summary.glm(g) :
observations with zero weight not used for calculating dispersion
2: In summary.glm(glm.object) :
observations with zero weight not used for calculating dispersion
>
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