Compute survey statistics on subsets of a survey defined by factors.
svyby(formula, by ,design,...)
# S3 method for default
svyby(formula, by, design, FUN, ..., deff=FALSE,keep.var = TRUE,
keep.names = TRUE,verbose=FALSE, vartype=c("se","ci","ci","cv","cvpct","var"),
drop.empty.groups=TRUE, covmat=FALSE, return.replicates=FALSE,
na.rm.by=FALSE, na.rm.all=FALSE,
multicore=getOption("survey.multicore"))
# S3 method for survey.design2
svyby(formula, by, design, FUN, ..., deff=FALSE,keep.var = TRUE,
keep.names = TRUE,verbose=FALSE, vartype=c("se","ci","ci","cv","cvpct","var"),
drop.empty.groups=TRUE, covmat=FALSE, influence=covmat,
na.rm.by=FALSE, na.rm.all=FALSE, multicore=getOption("survey.multicore"))# S3 method for svyby
SE(object,...)
# S3 method for svyby
deff(object,...)
# S3 method for svyby
coef(object,...)
# S3 method for svyby
confint(object, parm, level = 0.95,df =Inf,...)
unwtd.count(x, design, ...)
A formula specifying the variables to pass to
FUN (or a matrix, data frame, or vector)
A formula specifying factors that define subsets, or a list of factors.
A svydesign or svrepdesign object
A function taking a formula and survey design object as its first two arguments.
Other arguments to FUN
Request a design effect from FUN
If FUN returns a svystat object, extract
standard errors from it
Define row names based on the subsets
If TRUE, print a label for each subset as it is
processed.
Report variability as one or more of standard error, confidence interval, coefficient of variation, percent coefficient of variation, or variance
If FALSE, report NA for empty
groups, if TRUE drop them from the output
If true, omit groups defined by NA values of the
by variables
If true, check for groups with no non-missing
observations for variables defined by formula and treat these groups
as empty
If TRUE, compute covariances between estimates for
different subsets. Allows svycontrast to be used on
output. Requires that FUN supports either
return.replicates=TRUE or influence=TRUE
Only for replicate-weight designs. If
TRUE, return all the replicates as the "replicates" attribute of the result
Return the influence functions of the result
Use multicore package to distribute subsets over
multiple processors?
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
the confidence level required.
degrees of freedom for t-distribution in confidence
interval, use degf(design) for number of PSUs minus number of
strata
An object of class "svyby"
An object of class "svyby": a data frame showing the factors and the results of FUN.
For unwtd.count, the unweighted number of non-missing observations in the data matrix specified by x for the design.
The variance type "ci" asks for confidence intervals, which are produced
by confint. In some cases additional options to FUN will
be needed to produce confidence intervals, for example,
svyquantile needs ci=TRUE or keep.var=FALSE.
unwtd.count is designed to be passed to svyby to report
the number of non-missing observations in each subset. Observations
with exactly zero weight will also be counted as missing, since that's
how subsets are implemented for some designs.
Parallel processing with multicore=TRUE is useful only for
fairly large problems and on computers with sufficient memory. The
multicore package is incompatible with some GUIs, although the
Mac Aqua GUI appears to be safe.
svytable and ftable.svystat for
contingency tables, ftable.svyby for pretty-printing of svyby
# NOT RUN {
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,vartype="ci")
## 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$stype), dclus1, svymean)
svyby(~api99+api00, ~stype, dclus1, svymean, deff=TRUE,vartype="ci")
svyby(~api99+api00, ~stype+sch.wide, dclus1, svymean, keep.var=FALSE)
## report raw number of observations
svyby(~api99+api00, ~stype+sch.wide, dclus1, unwtd.count, keep.var=FALSE)
rclus1<-as.svrepdesign(dclus1)
svyby(~api99, ~stype, rclus1, svymean)
svyby(~api99, ~stype, rclus1, svyquantile, quantiles=0.5)
svyby(~api99, list(school.type=apiclus1$stype), rclus1, svymean, vartype="cv")
svyby(~enroll,~stype, rclus1,svytotal, deff=TRUE)
svyby(~api99+api00, ~stype+sch.wide, rclus1, svymean, keep.var=FALSE)
##report raw number of observations
svyby(~api99+api00, ~stype+sch.wide, rclus1, unwtd.count, 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))
str(svyby(~api99, ~stype, rclus1, svymean,return.replicates=TRUE))
tots<-svyby(~enroll, ~stype, dclus1, svytotal,covmat=TRUE)
vcov(tots)
svycontrast(tots, quote(E/H))
## 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)
confint(a, df=degf(rclus1))
## ratio estimates
svyby(~api.stu, by=~stype, denominator=~enroll, design=dclus1, svyratio)
ratios<-svyby(~api.stu, by=~stype, denominator=~enroll, design=dclus1, svyratio,covmat=TRUE)
vcov(ratios)
## empty groups
svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean)
svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean,drop.empty.groups=FALSE)
# }
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