Last chance! 50% off unlimited learning
Sale ends in
fullmatch(distance, min.controls = 0, max.controls = Inf,
omit.fraction = NULL, tol = 0.001, subclass.indices = NULL)min.controls is not a whole number, the reciprocal of a whole
number, or zero, then it is rounded downmax.controls is not a whole number, the reciprocal of a
whole number, or Inf, then it is rounded <omit.fraction is a positive fraction less than one, then
fullmatch leaves up to that fraction of the control reservoir
unmatched. fullmatch may
solve a slightly different matching problem than the one
specified, in which the match generated by
fullmatch may not coincide with an optimal solution of
the specified problem.c('optmatch',
'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, if matching within subclasses, or the string
'm' if not; (ii) the string .; and (iii) a
nonnegative integer or the string NA. 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.)distance indicate
permissible matches, with smaller
discrepancies indicating more desirable matches.
Matrix distance must have row and column names.
Consider using mdist to generate the distances.
fullmatch tries to guess the
order in which units would have been given in a data frame, and to
order the factor that it returns accordingly. If the dimnames of
distance, or the matrices it lists, are not simply row numbers
of the data frame you're working with, then you should compare the
names of fullmatch's output to your row names in order to be sure
things are in the proper order. You can relieve yourself of these
worries by using mdist (or makedist, pscore.dist,
or mahal.dist, which [as of version 0.6] are dispatched
as needed by mdist) to produce the distances, as
it passes the ordering of units to fullmatch, which then uses
it to order its outputs.
The value of tol can have a substantial effect on
computation time; with smaller values, computation takes longer.
Not every tolerance can be met, and how small a tolerance is too small
varies with the machine and with the details of the problem. If
fullmatch can't guarantee that the tolerance is as small as the
given value of argument tol, then matching proceeds but a
warning is issued. Rosenbaum, P. (1991),
matched, mdist,
caliperdata(nuclearplants)
### Full matching on a Mahalanobis distance
mhd <- mdist(pr ~ t1 + t2, data = nuclearplants)
( fm1 <- fullmatch(mhd) )
summary(fm1)
### Full matching with restrictions
( fm2 <- fullmatch(mhd, min=.5, max=4) )
summary(fm2)
### Full matching to half of available controls
( fm3 <- fullmatch(mhd, omit.fraction=.5) )
summary(fm3)
### Full matching within a propensity score caliper.
ppty <- glm(pr~.-(pr+cost), family=binomial(), data=nuclearplants)
### Note that units without counterparts within the
### caliper are automatically dropped.
( fm4 <- fullmatch(mhd+caliper(1, ppty)) )
summary(fm4)
### Propensity balance assessment. Requires RItools package.
library(RItools) ; summary(fm4,ppty)
### Creating a data frame with the matched sets attached.
### mdist(), caliper() and the like cooperate with fullmatch()
### to make sure observations are in the proper order:
all.equal(names(fm4), row.names(nuclearplants))
### So our data frame including the matched sets is just
cbind(nuclearplants, matches=fm4)
### In contrast, if your matching distance is an ordinary matrix
### (as earlier versions of optmatch required), you'll
### have to align it by observation name with your data set.
cbind(nuclearplants, matches = fm4[row.names(nuclearplants)])Run the code above in your browser using DataLab