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MIcombine {mitools}

Multiple imputation inference
Package: 
mitools
Version: 
2.2

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 class 'default':
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.

Values

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))

Documentation reproduced from package mitools, version 2.2. License: GPL-2