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 class 'vector'
exactMatch(x, treatment)
# S4 method for class '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
xindicating 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.frameormatrixthat contains the variables used in the formulax.- 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.
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, d
#> 1.2 b, f
#> 1.3 c, e