Analyse multiple imputations

Share:

Description

Performs a survey analysis on each of the designs in a svyimputationList objects and returns a list of results suitable for MIcombine. The analysis may be specified as an expression or as a function.

Usage

1
2
3
4
## S3 method for class 'svyimputationList'
with(data, expr, fun, ...,multicore=getOption("survey.multicore"))
## S3 method for class 'svyimputationList'
subset(x, subset,...)

Arguments

data,x

A svyimputationList object

expr

An expression giving a survey analysis

fun

A function taking a survey design object as its argument

...

for future expansion

multicore

Use multicore package to distribute imputed data sets over multiple processors?

subset

An logical expression specifying the subset

Value

A list of the results from applying the analysis to each design object.

See Also

MIcombine, in the mitools package

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
library(mitools)
data.dir<-system.file("dta",package="mitools")
files.men<-list.files(data.dir,pattern="m.\\.dta$",full=TRUE)
men<-imputationList(lapply(files.men, foreign::read.dta))
files.women<-list.files(data.dir,pattern="f.\\.dta$",full=TRUE)
women<-imputationList(lapply(files.women, foreign::read.dta))
men<-update(men, sex=1)
women<-update(women,sex=0)
all<-rbind(men,women)

designs<-svydesign(id=~id, strata=~sex, data=all)
designs

results<-with(designs, svymean(~drkfre))

MIcombine(results)

summary(MIcombine(results))

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.