Multiple imputation inference
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.
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,...)
results |
A list of results from inference on separate imputed datasets |
variances |
If |
call |
A function call for labelling the results |
df.complete |
Complete-data degrees of freedom |
... |
Other arguments, not used |
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.
An object of class MIresult with summary and
print methods
~put references to the literature/web site here ~
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))
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