R: Missing data imputation via the 'mix' package

imputeData {mclust} | R Documentation

## Missing data imputation via the mix package

### Description

Imputes missing data using the mix package.

### Usage

    imputeData(data, categorical = NULL, seed = NULL, verbose = interactive())

### Arguments

`data` | A numeric vector, matrix, or data frame of observations containing missing values. Categorical variables are allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`categorical` | A logical vectors whose ith entry is `TRUE` if the ith variable or column of `data` is to be interpreted as categorical and `FALSE` otherwise. The default is to assume that a variable is to be interpreted as categorical only if it is a factor.
`seed` | A seed for the function `rngseed` that is used to initialize the random number generator in mix. By default, a seed is chosen uniformly in the interval `(.Machine$integer.max/1024, .Machine$integer.max)`.
`verbose` | A logical, if `TRUE` reports info about iterations of the algorithm.

### Value

A dataset of the same dimensions as `data` with missing values filled in.

### References

Schafer J. L. (1997). Analysis of Imcomplete Multivariate Data, Chapman and Hall.

### See Also

`imputePairs`

### Examples

    ## Not run:
    # Note that package 'mix' must be installed
    data(stlouis, package = "mix")

    # impute the continuos variables in the stlouis data
    stlimp <- imputeData(stlouis[,-(1:3)])

    # plot imputed values
    imputePairs(stlouis[,-(1:3)], stlimp)

    ## End(Not run)

[Package mclust version 5.4.2 Index]