R: Estimate a Model Pooling Over the Imputed Datasets

06pool {mi} | R Documentation

## Estimate a Model Pooling Over the Imputed Datasets

### Description

This function estimates a chosen model, taking into account the additional uncertainty that arises due to a finite number of imputations of the missing data.

### Usage

    pool(formula, data, m = NULL, FUN = NULL, ...)

### Arguments

`formula` | a `formula` in the same syntax as used by `glm`
`data` | an object of `mi-class`
`m` | number of completed datasets to average over, which if `NULL` defaults to the number of chains used in `mi`
`FUN` | Function to estimate models or `NULL` which uses the same function as used in the `fit_model-methods` for the dependent variable
`...` | further arguments passed to `FUN`

### Details

`FUN` is estimated on each of the `m` completed datasets according to the given `formula` and the results are combined using the Rubin Rules.

### Value

An object of class `"pooled"` whose definition is subject to change but it has a `summary` and `display` method.

### Author(s)

Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.

### See Also

`mi`

### Examples

    if(!exists("imputations", env = .GlobalEnv)) {
      imputations <- mi:::imputations # cached from example("mi-package")
    }
    analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace,
                     data = imputations)
    display(analysis)

[Package mi version 1.0 Index]