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,...)

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.

Value

An object of class MIresult with summary and print methods

References

~put references to the literature/web site here ~

Examples

data(smi)
models<-with(smi, glm(drinkreg~wave*sex,family=binomial()))
summary(MIcombine(models))
#> Multiple imputation results:
#>       with(smi, glm(drinkreg ~ wave * sex, family = binomial()))
#>       MIcombine.default(models)
#>                 results         se      (lower     upper) missInfo
#> (Intercept) -2.25974358 0.26830731 -2.78584855 -1.7336386      4 %
#> wave         0.24055250 0.06587423  0.11092461  0.3701804     12 %
#> sex          0.64905222 0.34919264 -0.03537187  1.3334763      1 %
#> wave:sex    -0.03725422 0.08609199 -0.20623121  0.1317228      7 %

betas<-MIextract(models,fun=coef)
vars<-MIextract(models, fun=vcov)
summary(MIcombine(betas,vars))
#> Multiple imputation results:
#>       MIcombine.default(betas, vars)
#>                 results         se      (lower     upper) missInfo
#> (Intercept) -2.25974358 0.26830731 -2.78584855 -1.7336386      4 %
#> wave         0.24055250 0.06587423  0.11092461  0.3701804     12 %
#> sex          0.64905222 0.34919264 -0.03537187  1.3334763      1 %
#> wave:sex    -0.03725422 0.08609199 -0.20623121  0.1317228      7 %