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.

## Usage

exactMatch(x, ...)

# S4 method for vector
exactMatch(x, treatment)

# S4 method for formula
exactMatch(x, data = NULL, subset = NULL, na.action = NULL, ...)

## Arguments

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 x indicating treatment and control for each unit in the study. TRUE or 1 represents a treatment unit, FALSE or 0 represents a control unit. NA units are excluded.

data

A data.frame or matrix that contains the variables used in the formula x.

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 NAs

## Value

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.

## Details

exactMatch creates a block diagonal matrix of 0s and Infs. 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.

caliper, antiExactMatch, match_on, fullmatch, pairmatch

## Author

Mark M. Fredrickson

## Examples


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)
#>        control
#> treated   H   I   J   K   L   M   N   O   P   Q   R   S   T   U   V   W   X   Y
#>       A   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
#>       B   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
#>       C   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
#>       D   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
#>       E   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
#>       F   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
#>       G   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
#>       a Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
#>       b Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
#>       c Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
#>        control
#> treated   Z   d   e   f
#>       A   0 Inf Inf Inf
#>       B   0 Inf Inf Inf
#>       C   0 Inf Inf Inf
#>       D   0 Inf Inf Inf
#>       E   0 Inf Inf Inf
#>       F   0 Inf Inf Inf
#>       G   0 Inf Inf Inf
#>       a Inf   0   0   0
#>       b Inf   0   0   0
#>       c Inf   0   0   0

### Blunt matches:
match.pt.em <- fullmatch(pt.em)
#> Warning: Without 'data' argument the order of the match is not guaranteed
#>     to be the same as your original data.
print(match.pt.em, grouped = TRUE)
#>  Group                                  Members
#>    0.1                                     A, Z
#>    0.2                                     B, Y
#>    0.3                                     C, X
#>    0.4                                     D, W
#>    0.5                                     E, V
#>    0.6                                     F, U
#>    0.7 G, H, I, J, K, L, M, N, O, P, Q, R, S, T
#>    1.1                                     a, f
#>    1.2                                     b, e
#>    1.3                                     c, d

### Combine the propensity scores with the subclasses:
match.ppty.em <- fullmatch(ppty.distances + pt.em)
#> Warning: Without 'data' argument the order of the match is not guaranteed
#>     to be the same as your original data.
print(match.ppty.em, grouped = TRUE)
#>  Group                                        Members
#>    0.1                                     A, D, F, V
#>    0.2                                        B, G, U
#>    0.3 C, H, J, K, L, M, N, O, P, Q, R, T, W, X, Y, Z
#>    0.5                                        E, I, S
#>    1.1                                           a, e
#>    1.2                                           b, d
#>    1.3                                           c, f