mitools (version 2.4)

MIcombine: 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, ...)
# S3 method for default
MIcombine(results,variances,call=sys.call(),df.complete=Inf,...)
# S3 method for 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

Value

An object of class MIresult with summary and print methods

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.

References

~put references to the literature/web site here ~

See Also

MIextract, with.imputationList

Examples

# NOT RUN {
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))
# }