svyby {survey} | R Documentation |
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, 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 |
.
covmat |
If |
return.replicates |
Only for replicate-weight designs. If
|
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.
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)) ## 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) ## empty groups svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean) svyby(~api00,~comp.imp+sch.wide,design=dclus1,svymean,drop.empty.groups=FALSE)