R: Apply a Function to a Object of Class mi

mipply {mi} | R Documentation

## Apply a Function to a Object of Class mi

### Description

This function is a wrapper around `sapply` that is invoked on the `data` slot of an object of `mi-class` and / or on an object of `missing_data.frame-class` after being coerced to a `data.frame`

### Usage

    mipply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE,
           columnwise = TRUE, to.matrix = FALSE)

### Arguments

`X` | Object of `mi-class`, `missing_data.frame-class`, `missing_variable-class`, `mi_list-class`, or `mdf_list-class`
`FUN` | Function to call
`...` | Further arguments passed to `FUN`, currently broken
`simplify` | If `TRUE`, coerces result to a vector or matrix if possible
`USE.NAMES` | ignored but included for compatibility with `sapply`
`columnwise` | logical indicating whether to invoke `FUN` on the columns of a `missing_data.frame` after coercing it to a `data.frame` or a `matrix` or to invoke `FUN` on the “whole” `data.frame` or `matrix`
`to.matrix` | Logical indicating whether to coerce each `missing_data.frame` to a numeric `matrix` or to a `data.frame`. The default is `FALSE`, in which case the `data.frame` will include `factor`s if any of the `missing_variable`s inherit from `categorical-class`

### Details

The `columnwise` and `to.matrix` arguments are the only additions to the argument list in `sapply`, see the Examples section for an illustration of their use. Note that functions such as `mean` only accept `numeric` inputs, which can produce errors or warnings when `to.matrix = FALSE`.

### Value

A list, vector, or matrix depending on the arguments

### 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

`sapply`

### Examples

    if(!exists("imputations", env = .GlobalEnv)) {
      imputations <- mi:::imputations # cached from example("mi-package")
    }
    round(mipply(imputations, mean, to.matrix = TRUE), 3)
    mipply(imputations, summary, columnwise = FALSE)

[Package mi version 1.0 Index]