Survey statistics on subsets
Compute survey statistics on subsets of a survey defined by factors.
svyby(formula, by ,design,...)
## Default S3 method:
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 class '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 class 'svyby'
SE(object,...)
## S3 method for class 'svyby'
deff(object,...)
## S3 method for class 'svyby'
coef(object,...)
## S3 method for class 'svyby'
confint(object, parm, level = 0.95,df =Inf,...)
unwtd.count(x, design, ...)formula,x |
A formula specifying the variables to pass to
|
by |
A formula specifying factors that define subsets, or a list of factors. |
design |
A |
FUN |
A function taking a formula and survey design object as its first two arguments. |
... |
Other arguments to |
deff |
Request a design effect from |
keep.var |
If |
keep.names |
Define row names based on the subsets |
verbose |
If |
vartype |
Report variability as one or more of standard error, confidence interval, coefficient of variation, percent coefficient of variation, or variance |
drop.empty.groups |
If |
na.rm.by |
If true, omit groups defined by |
.
na.rm.all |
If true, check for groups with no non-missing
observations for variables defined by |
covmat |
If |
return.replicates |
Only for replicate-weight designs. If
|
influence |
Return the influence functions of the result |
multicore |
Use |
parm |
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. |
level |
the confidence level required. |
df |
degrees of freedom for t-distribution in confidence
interval, use |
object |
An object of class |
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.
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 function works by making a lot of calls of the form
FUN(formula, subset(design, by==i)), where formula is
re-evaluated in each subset, so it is unwise to use data-dependent
terms in formula. In particular, svyby(~factor(a), ~b,
design=d, svymean), will create factor variables whose levels are
only those values of a present in each subset. Either use
update.survey.design to add variables to the design
object instead or specify the levels explicitly in the call to factor.
Asking for a design effect (deff=TRUE) from a function
that does not produce one will cause an error or incorrect formatting
of the output. The same will occur with keep.var=TRUE if the
function does not compute a standard error.
svytable and ftable.svystat for
contingency tables, ftable.svyby for pretty-printing of svyby
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.