survey (version 3.9-1)

svyby: Survey statistics on subsets

Description

Compute survey statistics on subsets of a survey defined by factors.

Usage

svyby(formula, by ,design,...)
## S3 method for class 'default':
svyby(formula, by, design, FUN, ..., deff=FALSE,keep.var = TRUE,
keep.names = TRUE,verbose=FALSE, vartype=c("se","cv","cvpct","var"),
 drop.empty.groups=TRUE, covmat=FALSE)
## S3 method for class 'svyby':
SE(object,...)
## S3 method for class 'svyby':
deff(object,...)
## S3 method for class 'svyby':
coef(object,...)

Arguments

formula

A formula specifying the variables to pass to FUN (or a matrix, data frame, or vector)

by

A formula specifying factors that define subsets, or a list of factors.

design

A svydesign or svrepdesign object

FUN

A function taking a formula and survey design object as its first two arguments.

...

Other arguments to FUN

deff

Request a design effect from FUN

keep.var

If FUN returns a svystat object, extract standard errors from it

keep.names

Define row names based on the subsets

verbose

If TRUE, print a label for each subset as it is processed.

vartype

Report variability as one or more of standard error, coefficient of variation, percent coefficient of variation, or variance

drop.empty.groups

If FALSE, report NA for empty groups, if TRUE drop them from the output

covmat

If TRUE, compute covariances between estimates for different subsets (currently only for replicate-weight designs). Allows svycontrast to be used on output.

object

An object of class "svyby"

Value

• An object of class "svyby": a data frame showing the factors and the results of FUN

See Also

svytable and ftable.svystat for contingency tables, ftable.svyby for pretty-printing of svyby

Examples

data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

svyby(~api99, ~stype, dclus1, svymean)
svyby(~api99, ~stype, dclus1, svyquantile, quantiles=0.5,ci=TRUE)
## without ci=TRUE svyquantile does not compute standard errors
svyby(~api99, ~stype, dclus1, svyquantile, quantiles=0.5, keep.var=FALSE)
svyby(~api99, list(school.type=apiclus1$stype), dclus1, svymean)
svyby(~api99+api00, ~stype, dclus1, svymean, deff=TRUE)
svyby(~api99+api00, ~stype+sch.wide, dclus1, svymean, keep.var=FALSE)

rclus1<-as.svrepdesign(dclus1)

svyby(~api99, ~stype, rclus1, svymean)
svyby(~api99, ~stype, rclus1, svyquantile, quantiles=0.5)
svyby(~api99, list(school.type=apiclus1$stype), rclus1, svymean, vartype="cv")
svyby(~enroll,~stype, rclus1,svytotal, deff=TRUE)
svyby(~api99+api00, ~stype+sch.wide, rclus1, svymean, keep.var=FALSE)

## comparing subgroups using covmat=TRUE
mns<-svyby(~api99, ~stype, rclus1, svymean,covmat=TRUE)
vcov(mns)
svycontrast(mns, c(E = 1, M = -1))


## extractor functions
(a<-svyby(~enroll, ~stype, rclus1, svytotal, deff=TRUE, verbose=TRUE, vartype=c("se","cv","cvpct","var")))
deff(a)
SE(a)
cv(a)
coef(a)

## ratio estimates
svyby(~api.stu, by=~stype, denominator=~enroll, design=dclus1, svyratio)

## empty groups
svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean)
svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean,drop.empty.groups=FALSE)