# Find the minimal exact match factors that will be feasible for a given maximum problem size.

### Description

The `exactMatch`

function creates a smaller matching problem by
stratifying observations into smaller groups. For a problem that is larger
than maximum allowed size, `minExactMatch`

provides a way to find the
smallest exact matching problem that will allow for matching.

### Usage

1 |

### Arguments

`x` |
The object for dispatching. |

`scores` |
Optional vector of scores that will be checked against a caliper width. |

`width` |
Optional width of a caliper to place on the scores. |

`maxarcs` |
The maximum problem size to attempt to fit. |

`...` |
Additional arguments for methods. |

### Details

`x`

is a formula of the form `Z ~ X1 + X2`

, where
`Z`

is indicates treatment or control status, and `X1`

and `X2`

are variables
can be converted to factors. Any additional arguments are passed to `model.frame`

(e.g., a `data`

argument containing `Z`

, `X1`

, and `X2`

).

The the arguments `scores`

and `width`

must be passed together.
The function will apply the caliper implied by the scores and the width while
also adding in blocking factors.

### Value

A factor grouping units, suitable for `exactMatch`

.