# ampute.mcar: Multivariate Amputation In A MCAR Manner

## Description

This function creates a missing data indicator for each pattern, based on a MCAR
missingness mechanism. The function is used in the multivariate amputation function
`ampute`

.

## Usage

ampute.mcar(P, patterns, prop)

## Arguments

P

A vector containing the pattern numbers of the cases's candidacies.
For each case, a value between 1 and #patterns is given. For example, a
case with value 2 is candidate for missing data pattern 2.

patterns

A matrix of size #patterns by #variables where `0`

indicates
a variable should have missing values and `1`

indicates a variable should
remain complete. The user may specify as many patterns as desired. One pattern
(a vector) is also possible. Could be the result of `ampute.default.patterns`

,
default will be a square matrix of size #variables where each pattern has missingness
on one variable only.

prop

A scalar specifying the proportion of missingness. Should be a value
between 0 and 1. Default is a missingness proportion of 0.5.

## Value

A list containing vectors with `0`

if a case should be made missing
and `1`

if a case should remain complete. The first vector refers to the
first pattern, the second vector to the second pattern, etcetera.