MImix: Multiple imputation summaries via mixture of normals
In MImix: Mixture summary method for multiple imputation

Description Usage Arguments Details Value Author(s) References Examples

Combines results of analyses on multiply imputed data sets using a mixture of normal distributions.

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MImix(results, ...)
## Default S3 method:
MImix(results,variances,weights = 1/length(results), percentiles = c(0.025, 0.5, 0.975), ...)

`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
`weights`	A vector of weights for each imputed dataset. The default is to use equal weights.
`percentiles`	A vector of percentiles to be returned from the mixture summary distribution. The default is to return the 2.5th, 50th, and 97.5th percentiles.
`...`	Other arguments, not used

This function combines results of analyses on multiply imputed data sets using a mixture of normal distributions according to the approach described in Steele, R.J., Wang, N., and Raftery A.E. (2009). This package contains a generic function default method, although other methods may be available in future releases. 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. This corresponds to the same structure that is used by MIcombine in the mitools package.

An list containing the desired percentiles from the mixture summary distribution.

Russell Steele, steele@math.mcgill.ca

Steele, R.J., Wang N., and Raftery, A.E. (2009) Inference from Multiple Imputation for Missing Data Using Mixtures of Normals Sociological Methodology Accepted.