match_on) Create
matching distancesDeprecated in favor of match_on
mdist(x, structure.fmla = NULL, ...)# S3 method for optmatch.dlist
mdist(x, structure.fmla = NULL, ...)
# S3 method for function
mdist(x, structure.fmla = NULL, data = NULL, ...)
# S3 method for formula
mdist(x, structure.fmla = NULL, data = NULL,
subset = NULL, ...)
# S3 method for glm
mdist(x, structure.fmla = NULL, standardization.scale = mad,
...)
# S3 method for bigglm
mdist(x, structure.fmla = NULL, data = NULL,
standardization.scale = mad, ...)
# S3 method for numeric
mdist(x, structure.fmla = NULL, trtgrp = NULL, ...)
The object to use as the basis for forming the mdist. Methods exist for formulas, functions, and generalized linear models.
A formula denoting the treatment variable on
the left hand side and an optional grouping expression on the
right hand side. For example, z ~ 1 indicates no
grouping. z ~ s subsets the data only computing distances
within the subsets formed by s. See method notes, below,
for additional formula options.
Additional method arguments. Most methods require a 'data' argument.
Data where the variables references in `x` live.
If non-NULL, the subset of `data` to be used.
A function to scale the distances; by default uses `mad`.
Dummy variable for treatment group membership.
Object of class optmatch.dlist, which is suitable
to be given as distance argument to
fullmatch or pairmatch.
The mdist method provides three ways to construct a
matching distance (i.e., a distance matrix or suitably organized
list of such matrices): guided by a function, by a fitted model,
or by a formula. The class of the first argument given to
mdist determines which of these methods is invoked.
The mdist.function method takes a function of two
arguments. When called, this function will receive the treatment
observations as the first argument and the control observations as
the second argument. As an example, the following computes the raw
differences between values of t1 for treatment units (here,
nuclear plants with pr==1) and controls (here, plants with
pr==0), returning the result as a distance matrix:
sdiffs <- function(treatments, controls) {
abs(outer(treatments$t1, controls$t1, `-`))
}
The mdist.function method does similar things as the
earlier optmatch function makedist, although the interface
is a bit different.
The mdist.formula method computes the squared Mahalanobis
distance between observations, with the right-hand side of the
formula determining which variables contribute to the Mahalanobis
distance. If matching is to be done within strata, the
stratification can be communicated using either the
structure.fmla argument (e.g. ~ grp) or as part of
the main formula (e.g. z ~ x1 + x2 | grp).
An mdist.glm method takes an argument of class glm
as first argument. It assumes that this object is a fitted
propensity model, extracting distances on the linear propensity
score (logits of the estimated conditional probabilities) and, by
default, rescaling the distances by the reciprocal of the pooled
s.d. of treatment- and control-group propensity scores. (The
scaling uses mad, for resistance to outliers, by default;
this can be changed to the actual s.d., or rescaling can be
skipped entirely, by setting argument
standardization.scale to sd or NULL,
respectively.) A mdist.bigglm method works analogously
with bigglm objects, created by the bigglm function
from package ‘biglm’, which can handle bigger data sets
than the ordinary glm function can. In contrast with
mdist.glm it requires additional data and
structure.fmla arguments. (If you have enough data to
have to use bigglm, then you'll probably have to subgroup
before matching to avoid memory problems. So you'll have to use
the structure.fmla argument anyway.)
P.~R. Rosenbaum and D.~B. Rubin (1985), ‘Constructing a control group using multivariate matched sampling methods that incorporate the propensity score’, The American Statistician, 39 33--38.