The optmatch class describes the results of an optimal full matching
(using either fullmatch or pairmatch). For the
most part, these objects can be treated as factors.
The summary function quantifies optmatch objects on the effective sample
size, the distribution of distances between matched units, and how well the
match reduces average differences.
# S3 method for optmatch
summary(
object,
propensity.model = NULL,
...,
min.controls = 0.2,
max.controls = 5,
quantiles = c(0, 0.5, 0.95, 1)
)The optmatch object to summarize.
An optional propensity model (the result of
a call to glm) to use when summarizing the match. Using
the RItools package, an additional chi-squared test will
be performed on the average differences between treated and
control units on each variable used in the model. See the
xBalance function in the RItools package for more
details.
Additional arguments to pass to xBalance when
also passing a propensity model.
To minimize the the display of a groups with many treated and few controls, all groups with more than 5 treated units will be summarized as “5+”. This is the reciprocal of the default value (1/5 = 0.2). Lower this value to see more groups.
Like min.controls sets maximum group
sized displayed with respect to the number of controls. Raise
this value to see more groups.
A points in the ECDF at which the distances between units will be displayed.
optmatch.summary
optmatch objects descend from factor.
Elements of this vector correspond to members of the treatment and control
groups in reference to which the matching problem was posed, and are named
accordingly; the names are taken from the row and column names of
distance. Each element of the vector is either NA, indicating
unavailability of any suitable matches for that element, or the
concatenation of: (i) a character abbreviation of the name of the subclass
(as encoded using exactMatch) (ii) the string .; and
(iii) a non-negative integer. In this last place, positive whole numbers
indicate placement of the unit into a matched set and NA indicates
that all or part of the matching problem given to fullmatch was found
to be infeasible. The functions matched,
unmatched, and matchfailed distinguish these
scenarios.
Secondarily, fullmatch returns various data about the matching
process and its result, stored as attributes of the named vector which is
its primary output. In particular, the exceedances attribute gives
upper bounds, not necessarily sharp, for the amount by which the sum of
distances between matched units in the result of fullmatch exceeds
the least possible sum of distances between matched units in a feasible
solution to the matching problem given to fullmatch. (Such a bound
is also printed by print.optmatch and summary.optmatch.)