R: Summary measures for imputation models

summary.mitml {mitml} | R Documentation

## Summary measures for imputation models

### Description

Provides summary statistics and additional information on imputations in objects of class `mitml`.

### Usage

    ## S3 method for class 'mitml'
    summary(object, n.Rhat=3, goodness.of.appr=FALSE, autocorrelation=FALSE, ...)

### Arguments

`object` | An object of class `mitml` as produced by `panImpute` and `jomoImpute`.
`n.Rhat` | (optional) An integer denoting the number of sequences used for calculating the potential scale reduction factor. Default is to `3`.
`goodness.of.appr` | (optional) A logical flag indicating if the goodness of approximation should be printed. Default is to `FALSE` (see details).
`autocorrelation` | (optional) A logical flag indicating if the autocorrelation should be printed. Default is to `FALSE` (see details).
`...` | Not being used.

### Details

The `summary` method calculates summary statistics for objects of class `mitml` as produced by `panImpute` and `jomoImpute`. The output includes the potential scale reduction factor (PSRF, or \hat{R}) and (optionally) the goodness of approximation and autocorrelation.

The PSRF is calculated for each parameter of the imputation model and may be interpreted as a measure of convergence (Gelman and Rubin, 1992). Calculation of the PSRFs can be suppressed by setting `n.Rhat=NULL`. The PSRFs are not computed from different chains, but by dividing each chain from the imputation phase into a number of sequences as denoted by `n.Rhat`. This is slightly different from the original method proposed by Gelman and Rubin.

The goodness of approximation indicates what proportion of the posterior standard deviation is due to simulation error. For multiple imputation, the goodness of approximation is not essential; it should be considered only if posterior summaries, such as the EAP, are of interest.

The autocorrelation includes estimates of the autocorrelation in the parameter chains at lag 1 (i.e., for consecutive draws) and for lags k and 2k, where k is the number of iterations between imputations. For lag k and 2k, the autocorrelation is slightly smoothed to reduce the influence of noise on the estimates (see `plot.mitml`).

### Value

Returns an object of class `summary.mitml`. A print method is used for better readable console output.

### Author(s)

Simon Grund

### References

Gelman, A., and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457-472.

Hoff, P. D. (2009). A first course in Bayesian statistical methods. New York, NY: Springer.

### See Also

`panImpute`, `jomoImpute`, `plot.mitml`

### Examples

    data(studentratings)

    fml <- ReadDis + SES ~ ReadAchiev + (1|ID)
    imp <- panImpute(studentratings, formula=fml, n.burn=1000, n.iter=100, m=5)

    # print summary
    summary(imp)

[Package mitml version 0.3-5 Index]