Description Usage Arguments Details Value Author(s) See Also Examples

An exact match is one based on a factor. Within a level, all observations are allowed to be matched. An exact match can be combined with another distance matrix to create a set of matching subproblems.

1 2 3 4 5 6 7 8 | ```
exactMatch(x, ...)
## S4 method for signature 'vector'
exactMatch(x, treatment)
## S4 method for signature 'formula'
exactMatch(x, data = NULL, subset = NULL,
na.action = NULL, ...)
``` |

`x` |
A factor vector or a formula, used to select method. |

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

`treatment` |
A logical or binary vector the same length as |

`data` |
A |

`subset` |
an optional vector specifying a subset of observations to be used |

`na.action` |
A function which indicates what should happen when the data contain ‘NA’s |

`exactMatch`

creates a block diagonal matrix of 0s and
`Inf`

s. The pairs with 0 entries are within the same level of
the factor and legitimate matches. `Inf`

indicates units in
different levels. `exactMatch`

replaces the
`structure.fmla`

argument to several functions in previous
versions of optmatch. For the `factor`

method, the two
vectors `x`

and `treatment`

must be the same length. The
vector `x`

is interpreted as indicating the grouping factors
for the data, and the vector `treatment`

indicates whether a
unit is in the treatment or control groups. At least one of these
two vectors must have names. For the `formula`

method, the
`data`

argument may be omitted, in which case the method
attempts to find the variables in the environment from which the
function was called. This behavior, and the arguments `subset`

and `na.action`

, mimics the behavior of `lm`

.

A matrix like object, which is suitable to be given as
`distance`

argument to `fullmatch`

or
`pairmatch`

. The exact match will be only zeros and
`Inf`

values, indicating a possible match or no possible
match, respectively. It can be added to a another distance matrix
to create a subclassed matching problem.

Mark M. Fredrickson

`caliper`

, `antiExactMatch`

,
`match_on`

, `fullmatch`

,
`pairmatch`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
data(nuclearplants)
### First generate a standard propensity score
ppty <- glm(pr~.-(pr+cost), family = binomial(), data = nuclearplants)
ppty.distances <- match_on(ppty)
### Only allow matches within the partial turn key plants
pt.em <- exactMatch(pr ~ pt, data = nuclearplants)
as.matrix(pt.em)
### Blunt matches:
match.pt.em <- fullmatch(pt.em)
print(match.pt.em, grouped = TRUE)
### Combine the propensity scores with the subclasses:
match.ppty.em <- fullmatch(ppty.distances + pt.em)
print(match.ppty.em, grouped = TRUE)
``` |

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