MIcombine: Multiple imputation inference

In mitools: Tools for Multiple Imputation of Missing Data

Multiple imputation inference

Description

Combines results of analyses on multiply imputed data sets. A generic function with methods for imputationResultList objects and a default method. In addition to point estimates and variances, MIcombine computes Rubin's degrees-of-freedom estimate and rate of missing information.

Usage

MIcombine(results, ...)
## Default S3 method:
MIcombine(results,variances,call=sys.call(),df.complete=Inf,...)
## S3 method for class 'imputationResultList'
MIcombine(results,call=NULL,df.complete=Inf,...)

Arguments

results	A list of results from inference on separate imputed datasets
variances	If results is a list of parameter vectors, variances should be the corresponding variance-covariance matrices
call	A function call for labelling the results
df.complete	Complete-data degrees of freedom
...	Other arguments, not used

Details

The results argument in the default method may be either a list of parameter vectors or a list of objects that have coef and vcov methods. In the former case a list of variance-covariance matrices must be supplied as the second argument.

The complete-data degrees of freedom are used when a complete-data analysis would use a t-distribution rather than a Normal distribution for confidence intervals, such as some survey applications.

Value

An object of class MIresult with summary and print methods

References

~put references to the literature/web site here ~

See Also

MIextract, with.imputationList

Examples

data(smi)
models<-with(smi, glm(drinkreg~wave*sex,family=binomial()))
summary(MIcombine(models))

betas<-MIextract(models,fun=coef)
vars<-MIextract(models, fun=vcov)
summary(MIcombine(betas,vars))

mitools documentation built on April 18, 2024, 3 a.m.