| MIcombine {mitools} | R Documentation |
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 |
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))