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/dports/math/R-cran-survey/survey/
H A DNAMESPACE21 S3method(svyloglin,survey.design)
100 S3method(svyvar, survey.design)
124 S3method(svyglm,survey.design)
262 S3method(summary,survey.design)
263 S3method(summary,survey.design2)
266 S3method(summary,svyrep.design)
274 S3method(print,summary.survey.design)
275 S3method(print,summary.survey.design2)
278 S3method(print,summary.svyrep.design)
313 S3method(dim,survey.design)
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/dports/math/R-cran-survey/survey/man/
H A Dsubset.survey.design.Rd1 \name{subset.survey.design}
2 \alias{subset.survey.design}
4 \alias{[.survey.design}
6 \title{Subset of survey}
8 Restrict a survey design to a subpopulation, keeping the original design
14 \method{subset}{survey.design}(x, subset, ...)
19 \item{x}{A survey design object}
24 A new survey design object
33 summary(dsub)
38 summary(svyglm(x~I(x>4)+0,design=dfpc))
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H A Dsvychisq.Rd5 \alias{svytable.survey.design}
7 \alias{svychisq.survey.design}
9 \alias{summary.svytable}
10 \alias{print.summary.svytable}
11 \alias{summary.svreptable}
22 \method{svytable}{survey.design}(formula, design, Ntotal = NULL, round = FALSE,...)
24 \method{svychisq}{survey.design}(formula, design,
28 \method{summary}{svytable}(object,
31 \method{degf}{survey.design2}(design, ...)
37 \item{design}{survey object}
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H A DtrimWeights.Rd4 \alias{trimWeights.svyrep.design}
5 \alias{trimWeights.survey.design2}
15 \method{trimWeights}{survey.design2}(design, upper = Inf, lower = -Inf, strict=FALSE,...)
16 \method{trimWeights}{svyrep.design}(design, upper = Inf, lower = -Inf,compress=FALSE,...)
20 \item{design}{
21 A survey design object
43 A new survey design object with trimmed weights.
61 summary(weights(dclus1g))
63 summary(weights(dclus1t))
65 summary(weights(dclus1tt))
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H A DpostStratify.Rd4 \alias{postStratify.svyrep.design}
5 \alias{postStratify.survey.design}
7 \title{Post-stratify a survey }
16 \method{postStratify}{svyrep.design}(design, strata, population, partial = FALSE, compress=NULL,...)
17 \method{postStratify}{survey.design}(design, strata, population, partial = FALSE, ...)
21 \item{design}{A survey design with replicate weights}
63 A new survey design object.
70 analysis of survey data using poststratification information. Sankhya
93 summary(rclus1p)
100 summary(dclus1p)
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H A DwithPV.survey.design.Rd1 \name{withPV.survey.design}
2 \alias{withPV.survey.design}
11 \S3method{withPV}{survey.design}(mapping, data, action, rewrite=TRUE, ...)
19 A survey design object, as created by \code{svydesign} or \code{svrepdesign}
22 With \code{rewrite=TRUE}, a function taking a survey design object as
24 a function taking a survey design object as its only argument, or a
25 quoted expression with \code{.DESIGN} referring to the survey design object to be used.
49 oo<-options(survey.lonely.psu="remove")
53 action=quote(svyglm(maths~ST04Q01*(PCGIRLS+SMRATIO)+MATHEFF+OPENPS, design=des)),
56 summary(MIcombine(results))
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H A Dsvyglm.Rd3 \alias{svyglm.survey.design}
5 \alias{summary.svyglm}
6 \alias{summary.svrepglm}
16 Fit a generalised linear model to data from a complex survey design,
20 \method{svyglm}{survey.design}(formula, design, subset=NULL,
51 \code{summary.glm} }
164 summary(svyglm(api00~ell+meals+mobility, design=dstrat))
165 summary(svyglm(api00~ell+meals+mobility, design=dclus2))
166 summary(svyglm(api00~ell+meals+mobility, design=rstrat))
167 summary(svyglm(api00~ell+meals+mobility, design=rclus2))
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H A Dsvrepdesign.Rd6 \alias{[.svyrep.design}
7 \alias{image.svyrep.design}
10 \alias{summary.svyrep.design}
11 \alias{print.summary.svyrep.design}
13 \title{Specify survey design with replicate weights}
17 data structure for such a survey.
57 \item{x}{survey design with replicate weights}
121 \code{RSQLite} for SQLite). The survey design
133 \code{\link{as.svrepdesign}} on a \code{survey.design} object, or see
142 \code{summary}, \code{weights}, \code{image}.
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H A Destweights.Rd8 Creates or adjusts a two-phase survey design object using a logistic
19 \item{data}{twophase design object or data frame}
29 two-phase design object. The \code{strata} argument is used only to
33 With a two-phase design object, \code{estWeights} estimates the sampling
44 The effect on a two-phase design object is very similar to
49 A two-phase survey design object.
62 Lumley T, Shaw PA, Dai JY (2011) "Connections between survey calibration estimators and semiparamet…
72 summary(lm(log(Ozone)~Temp+Wind, data=airquality))
77 summary(svyglm(log(Ozone)~Temp+Wind,design=daq))
82 summary(svyglm(log(Ozone)~Temp+Wind,design=d2aq))
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H A Dsvysurvreg.Rd3 \alias{svysurvreg.survey.design}
6 Fit accelerated failure models to survey data
9 …survey data, and then computes correct standard errors by linearisation. It has the same argument…
12 \method{svysurvreg}{survey.design}(formula, design, weights=NULL, subset=NULL, ...)
19 \item{design}{
20 Survey design object, including two-phase designs
51 model <- svysurvreg(Surv(time, status>0)~bili+protime+albumin, design=dpbc, dist="weibull")
52 summary(model)
57 \keyword{survey}% use one of RShowDoc("KEYWORDS")
H A Dsvymle.Rd5 \alias{summary.svymle}
11 predictors to data from a complex sample survey and computes the
24 \item{design}{ a \code{survey.design} object }
46 The \code{design} object contains all the data and design information
47 from the survey, so all the formulas refer to variables in this object.
68 The usual variance estimator for MLEs in a survey sample is a `sandwich'
110 summary(m0)
111 summary(m1,stderr="model")
112 summary(m2)
165 summary(m)
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H A Dsvyratio.Rd5 \alias{svyratio.svyrep.design}
6 \alias{svyratio.survey.design}
7 \alias{svyratio.survey.design2}
18 survey samples. Estimating domain (subpopulation) means can be done
22 \method{svyratio}{survey.design2}(numerator=formula, denominator,
25 \method{svyratio}{svyrep.design}(numerator=formula, denominator, design,
40 \item{design}{survey design object}
68 as shown in the output of \code{summary(design)}.
70 When \code{design} is a two-phase design, stratification will be on
91 ## survey design objects
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H A Dwith.svyimputationList.Rd7 Performs a survey analysis on each of the designs in a
13 \method{with}{svyimputationList}(data, expr, fun, ...,multicore=getOption("survey.multicore"))
19 \item{expr}{An expression giving a survey analysis}
20 \item{fun}{A function taking a survey design object as its argument }
28 A list of the results from applying the analysis to each design object.
52 summary(MIcombine(results))
60 \keyword{survey }% __ONLY ONE__ keyword per line
/dports/math/R-cran-survey/survey/tests/
H A Dlonely.psu.Rout.save19 > ## lonely PSUs by design
20 > library(survey)
31 > summary(ds)
32 Stratified Independent Sampling design (with replacement)
56 Error in jknweights(design$strata[, 1], design$cluster[, 1], fpc = fpc, :
90 > summary(ds)
91 Stratified Independent Sampling design
160 > summary(ds1)
161 Stratified Independent Sampling design (with replacement)
206 > summary(ds1)
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H A Dnwts.R4 library(survey)
30 summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs2))
31 summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs8))
32 summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=gccs8))
35 summary(svyglm(rel~factor(stage),
36 family=quasibinomial,design=subset(dccs8,histol==1)))
37 summary(svyglm(rel~factor(stage),
38 family=quasibinomial,design=subset(gccs8,histol==1)))
H A Dnwts.Rout.save23 > library(survey)
28 Attaching package: ‘survey’
59 > summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs2))
86 > summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=dccs8))
113 > summary(svyglm(rel~factor(stage)*factor(histol),family=quasibinomial,design=gccs8))
142 > summary(svyglm(rel~factor(stage),
167 2: In summary.glm(g) :
169 3: In summary.glm(glm.object) :
175 > summary(svyglm(rel~factor(stage),
199 1: In summary.glm(g) :
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/dports/math/R-cran-survey/survey/vignettes/
H A Dsurvey.Rnw3 %\VignetteIndexEntry{A survey analysis example}
6 \title{A survey analysis example}
27 library(survey)
34 summary(dclus1)
37 We can compute summary statistics to estimate the mean, median, and
42 design object containing the data.
56 svyratio(~api.stu, ~enroll, design=subset(dclus1, stype=="H"))
74 svyby(~ell+meals, ~stype, design=dclus1, svymean)
80 regmodel <- svyglm(api00~ell+meals,design=dclus1)
82 summary(regmodel)
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H A Ddomain.Rnw20 survey design objects automatically do the necessary adjustments, but
24 \texttt{survey/tests/domain.R}.
30 library(survey)
34 svymean(~x,design=dsub)
37 The \texttt{subset} function constructs a survey design object with
42 svyby(~x,~I(x>4),design=dfpc, svymean)
50 summary(svyglm(x~I(x>4)+0,design=dfpc))
62 for observations not in the domain. For most survey design objects
84 summary(svyglm(api00~comp.imp-1, gclus1))
94 summary(svyglm(y1~I(y1>40)+0,dmu284))
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/dports/math/R-cran-survey/survey/inst/doc/
H A Dsurvey.Rnw3 %\VignetteIndexEntry{A survey analysis example}
6 \title{A survey analysis example}
27 library(survey)
34 summary(dclus1)
37 We can compute summary statistics to estimate the mean, median, and
42 design object containing the data.
56 svyratio(~api.stu, ~enroll, design=subset(dclus1, stype=="H"))
74 svyby(~ell+meals, ~stype, design=dclus1, svymean)
80 regmodel <- svyglm(api00~ell+meals,design=dclus1)
82 summary(regmodel)
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H A Ddomain.Rnw20 survey design objects automatically do the necessary adjustments, but
24 \texttt{survey/tests/domain.R}.
30 library(survey)
34 svymean(~x,design=dsub)
37 The \texttt{subset} function constructs a survey design object with
42 svyby(~x,~I(x>4),design=dfpc, svymean)
50 summary(svyglm(x~I(x>4)+0,design=dfpc))
62 for observations not in the domain. For most survey design objects
84 summary(svyglm(api00~comp.imp-1, gclus1))
94 summary(svyglm(y1~I(y1>40)+0,dmu284))
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H A Dsurvey.R6 library(survey)
14 summary(dclus1)
30 svyratio(~api.stu, ~enroll, design=subset(dclus1, stype=="H"))
43 svyby(~ell+meals, ~stype, design=dclus1, svymean)
49 regmodel <- svyglm(api00~ell+meals,design=dclus1)
50 logitmodel <- svyglm(I(sch.wide=="Yes")~ell+meals, design=dclus1, family=quasibinomial())
51 summary(regmodel)
52 summary(logitmodel)
H A Depi.R6 library(survey)
36 summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs2))
46 summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=dccs8))
47 summary(svyglm(rel~factor(stage)*factor(histol),family=binomial,design=gccs8))
53 library(survey)
71 design=dcch)
104 design=dBarlow)
112 design=dWacholder)
125 design=d_BorganII))
133 design=d_BorganIIps)
H A Ddomain.R6 library(survey)
10 svymean(~x,design=dsub)
16 svyby(~x,~I(x>4),design=dfpc, svymean)
22 summary(svyglm(x~I(x>4)+0,design=dfpc))
41 summary(svyglm(api00~comp.imp-1, gclus1))
52 summary(svyglm(y1~I(y1>40)+0,dmu284))
65 summary(svyglm(rel~I(age>36)+0, dccs8))
/dports/devel/R-cran-broom/broom/man/
H A Dglance.svyglm.Rd2 % Please edit documentation in R/survey-tidiers.R
10 \item{x}{A \code{svyglm} object returned from \code{\link[survey:svyglm]{survey::svyglm()}}.}
37 Glance does not calculate summary measures. Rather, it farms out these
48 if (requireNamespace("survey", quietly = TRUE)) {
50 library(survey)
55 # survey design
66 m <- survey::svyglm(
68 design = dstrat,
82 \code{\link[survey:svyglm]{survey::svyglm()}}, \code{\link[stats:glm]{stats::glm()}}, \link[survey:…
89 \code{\link{glance.summary.lm}()},
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/dports/math/R-cran-survey/survey/tests/testoutput/
H A Dapi.Rout.saved18 > library(survey)
20 Attaching package: 'survey'
26 > options(survey.replicates.mse=TRUE)
29 api> library(survey)
42 api> summary(dstrat)
52 design.PSU 100 50 50
77 api> summary(dclus1)
105 api> summary(dclus2)
232 > options(survey.replicates.mse=FALSE)
235 api> library(survey)
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