optmatch
Functions for Optimal Matching
Distance based bipartite matching using the RELAX-IV minimum cost flow solver, oriented to matching of treatment and control groups in observational studies. Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.
The optmatch package implements the optimal full matching algorithm for
bipartite matching problems. Given a matrix describing the distances between
two groups (where one group is represented by row entries, and the other by
column entries), the algorithm finds a matching between units that
minimizes the average within grouped distances. This algorithm is a popular
choice for covariate balancing applications (e.g. propensity score matching),
but it also can be useful for design stage applications such as blocking. For
more on the application and its implementation, see:
Hansen, B.B. and Klopfer, S.O. (2006) Optimal full matching and
related designs via network flows, JCGS 15 609-627.
optmatch is available on CRAN:
> library("optmatch")
Using Optmatch
In addition to the optimal full matching algorithm, the package contains useful functions for generating distance specifications, combining and editing distance specifications, and summarizing and displaying matches. This walk through shows how to use these tools in your matching workflow.
Simulated data
Before we start, let's generate some simulated data. We will have two groups,
the "treated" and "control" groups. Without our knowledge, nature assigned
units from a pool into one of these two groups. The probability of being a
treated unit depends on some covariates. In the vector Z, let a 1 denote
treated units and 0 denote control units
set.seed(20120111) # set this to get the exact same answers as I do
n <- 26 # chosen so we can divide the alphabet in half
W <- data.frame(w1 = rbeta(n, 4, 2), w2 = rbinom(n, 1, p = .33))
# nature assigns to treatment
tmp <- numeric(n)
tmp[sample(1:n, prob = W$w1^(1 + W$w2), size = n/2)] <- 1
W$z <- tmp
# for convenience, let's give the treated units capital letter names
tmp <- character(n)
tmp[W$z == 1] <- LETTERS[1:(n/2)]
tmp[W$z == 0] <- letters[(26 - n/2 + 1):26]
rownames(W) <- tmp
As we can see with a simple table and plot, these groups are not balanced on the covariates, as they would be (in expectation) with a randomly assigned treatment.
table(W$w2, W$z)
library(lattice) ; densityplot(W$w1, groups = W$z)
The next steps use the covariates to pair up similar treated and control
units. For more on assessing the amount and severity of imbalance between
groups on observed covariates, see the
RItools R package.
Setting up distances
These two groups are different, but how different are individual treated units
from individual control units? In answering this question, we will produce
several distance specifications: matrices of treated units (rows) by control
units (columns) with entries denoting distances. optmatch provides several
ways of generating these matrices so that you don't have to do it by hand.
Let's begin with a simple Euclidean distance on the space defined by W:
distances <- list()
distances$euclid <- match_on(z ~ w1 + w2, data = W, method = "euclidean")
The method argument tells the match_on function how to compute the
distances over the space defined by the formula. The default method extends the
simple Euclidean distance by rescaling the distances by the covariance of the
variables, the Mahalanobis
distance:
distances$mahal <- match_on(z ~ w1 + w2, data = W)
You can write additional distance computation functions. See the documentation
for match_on for more details on how to create these functions.
To create distances, we could also try regressing the treatment indicator on
the covariates and computing the difference distance for each treated and
control pair. To make this process easier, match_on has methods for glm
objects (and for big data problems, bigglm objects):
propensity.model <- glm(z ~ w1 + w2, data = W, family =
binomial())
distances$propensity <- match_on(propensity.model)
The glm method is a wrapper around the numeric method for match_on. The
numeric method takes a vector of scores (for example, the linear prediction
for each unit from the model) and a vector indicating treatment status (z)
for each unit. This method returns the absolute difference between each
treated and control pair on their scores (additionally,
the glm method rescales the data before invoking the numeric method). If
you wish to fit a "caliper" to your distance matrix, a hard limit on allowed
distances between treated and control units, you can pass a caliper
argument, a scalar numeric value. Any treated and control pair that is larger
than the caliper value will be replaced by Inf, an unmatchable value. The
caliper argument also applies to glm method. Calipers are covered in more
detail in the next section.
The final convenience method of match_on is using an arbitrary function. This
function is probably most useful for advanced users of optmatch. See the
documentation of the match_on function for more details on how to write your
own arbitrary computation functions.
Combining and editing distances
We have created several representations of the matching problem, using Euclidean distance, Mahalanobis distance, the estimated propensity score, and an arbitrary function. We can combine these distances into single metric using standard arithmetic functions:
distances$all <- with(distances, euclid + mahal + propensity)
You may find it convenient to work in smaller pieces at first and then stitch
the results together into a bigger distance. The rbind and cbind functions let us
add additional treated and control entries to a distance specification for
each of the existing control and treated units, respectively. For example, we
might want to combine a Mahalanobis score for units n through s with a
propensity score for units t through z:
W.n.to.s <- W[c(LETTERS[1:13], letters[14:19]),]
W.t.to.z <- W[c(LETTERS[1:13], letters[20:26]),]
mahal.n.to.s <- match_on(z ~ w1 + w2, data = W.n.to.s)
ps.t.to.z <- match_on(glm(z ~ w1 + w2, data = W.t.to.z, family = binomial()))
distances$combined <- cbind(mahal.n.to.s, ps.t.to.z)
The exactMatch function creates "stratified" matching problems, in which
there are subgroups that are completely separate. Such matching problems are
often much easier to solve than problems where a treated unit could be
connected to any control unit.
There is another method for creating reduced matching problems. The caliper
function compares each entry in an existing distance specification and
disallows any that are larger than a specified value. For example, we can trim
our previous combined distance to anything smaller than the median value:
distances$median.caliper <- caliper(distances$all, median(distances$all))
distances$all.trimmed <- with(distances, all + median.caliper)
Like the exactMatch function, the results of caliper used the sparse
matrix representation mentioned above, so can be very efficient for large,
sparse problems. As noted previously, if using the glm or numeric methods
of match_on, passing the caliper's width in the caliper argument can be more
efficient.
Speeding up computation
In addition to the space advantages of only storing the finite entries in a
sparse matrix, the results of exactMatch and caliper can be used to speed
up computation of new distances. The match_on function that we saw earlier
has an argument called within that helps filter the resulting
computation to only the finite entries in the within matrix. Since exactMatch and caliper
use finite entries denote valid pairs, they make excellent sources of
the within argument.
Instead of creating the entire Euclidean distance matrix and then filtering
out cross-strata matches, we use the results of exactMatch to compute only
the interesting cases:
tmp <- exactMatch(z ~ w2, data = W)
distances$exact <- match_on(z ~ w1, data = W, within = tmp)
Users of previous versions of optmatch may notice that the within
argument is similar to the old structure.formula argument. Like
within, structure.formula focused distance on within strata pairs.
Unlike structure.formula, the within argument allows using any
distance specification as an argument, including those created with caliper. For
example, here is the Mahalanobis distance computed only for units that differ
by less than one on the propensity score.
distances$mahal.trimmed <- match_on(z ~ w1 + w2, data = W,
within = match_on(propensity.model, caliper = 1))
Generating the match
Now that we have generated several distances specifications, let's put them to use. Here is the simplest way to evaluate all distances specifications:
matches <- lapply(distances, function(x) { fullmatch(x, data = W) })
The result of the matching process is a named factor, where the names
correspond to the units (both treated and control) and the levels of the
factors are the matched groups. Including the data argument is highly
recommended. This argument will make sure that the result of fullmatch will
be in the same order as the original data.frame that was used to build the
distance specification. This will make appending the results of fullmatch
on to the original data.frame much more convenient.
The fullmatch function as several arguments for fine tuning the allowed
ratio of treatment to control units in a match, and how much of the pool to
throw away as unmatchable. One common pattern for these arguments are pairs:
one treated to one control unit. Not every distance specification is amendable
to this pattern (e.g. when there are more treated units than control units in
exactMatch created stratum). However, it can be done with the Mahalanobis
distance matrix we created earlier:
mahal.match <- pairmatch(distances$mahal, data = W)
Like fullmatch, pairmatch also allows fine tuning the ratio of matches to
allow larger groupings. It is can be helpful as it computes what percentage of
the group to throw away, giving better odds of successfully finding a matching
solution.
Once one has generated a match, you may wish to view the results. The results
of calls to fullmatch or pairmatch produce optmatch objects (specialized
factors). This object has a special option to the print method which groups
the units by factor level:
print(mahal.match, grouped = T)
If you wish to join the match factor back to the original data.frame:
W.matched <- cbind(W, matches = mahal.match)
Make sure to include the data argument to fullmatch or pairmatch,
otherwise results are not guaranteed to be in the same order as your original
data.frame or matrix.
Using a development version of Optmatch
This section will help you get the latest development version of optmatch and
start using the latest features. Before starting, you should know which branch
you wish to install. Currently, the "master" branch is the main code base.
Additional features are added in their own branches. A list of branches is
available at (the optmatch project
page)[https://github.com/markmfredrickson/optmatch].
Installing a development version
You must have the Fortran extensions for package building included. These can be had from CRAN: OS X, Windows.
We recommend using dev_mode from the devtools package to install
in-development version of the package so that you can keep the current CRAN
version as the primary package. Activating dev_mode creates a secondary
library of packages which can only be accessed while in dev_mode. Packages
normally installed can still be used, but if different versions are installed
normally and in dev_mode, the dev_mode version takes precedent if in
dev_mode.
Install and load the devtools package:
> install.packages("devtools")
> library("devtools")
Activate dev_mode:
> dev_mode()
d>
Note that the prompt changes from > to d> to let you know you're in
dev_mode. Now choose the development branch you want to use. To install
master:
d> install_github("markmfredrickson/optmatch")
Either way, the package is then loaded in the usual fashion, provided you're
still in dev_mode:
d> library(optmatch)
Once you've done this you can disable dev_mode as follows
d> dev_mode()
>
The development version of the package remains loaded.
Note that if you load the package -- ie, enter library(optmatch) (when the
package hasn't already been loaded otherwise) -- while not in dev_mode, then
you'll get whatever version of the package may be installed in your library
tree, not this development version.
If you want to switch between versions of RItools, we suggest re-starting R.
Developing for Optmatch
You may use RStudio to develop for Optmatch, by opening the optmatch.Rproj file.
We suggest you ensure all required dependencies are installed by running
devtools::install_deps(dependencies = TRUE)We prefer changes that include unit tests demonstrating the problem or showing
how the new feature should be added. The test suite uses the
testthat package to write and run tests.
(Please ensure you have the latest version of testthat (or at least v0.11.0),
as older versions stored the tests in a different directory, and may not
test properly.) See the tests/testthat directory for examples. You can run
the test suite via Build -> Test Package.
New features should include inline Roxygen documentation.
You can generate all .Rd documents from the Roxygen code using Build ->
Document.
Finally, you can use Build -> Build and Reload or Build -> Clean and Rebuild to
load an updated version of optmatch in your current RStudio session.
Alternatively, to install the developed version permanently, use Build -> Build
Binary Version, followed by
install.packages("../optmatch_VERSION.tgz", repo=NULL)You can revert back to the current CRAN version by
remove.packages("optmatch")install.packages("optmatch")If you prefer not to use RStudio, you can develop using Make.
make test: Run the full test suite.make document: Update all documentation from Roxygen inline comments.make interactive: Start up an interactive session withoptmatchloaded.make check: RunR CMD checkon the packagemake build: Build a binary package.make vignette: Builds any vignettes invignettes/directorymake clean: Removes files built bymake vignette,make documentormake check. Should not be generally necessary, but can be useful for debugging.
When your change is ready, make a pull request on github.
News
CHANGES IN OPTMATCH VERSION 0.9-8
- If pairmatch is asked to match within a stratum with fewer eligible controls
than
controlstimes the number of treatments, it now attempts to match in that stratum by leaving out some of the treatment units. (#116) - The treatment indicator must be either numeric 0/1 (1 for treatment, 0 for control) or logical (TRUE for treatment, FALSE for control).
- Support for any other type of treatment vector (factors, character, other numerical values)
has been deprecated. You can easily update treatment vectors using conditional
statements, e.g. if you have a character "T" and "C",
treatment_new = treatment == "T". - The treatment indicator can now include NA's. Any observations with NA treatment
status will be excluded from distances matrices and will never match. WARNING:
if the
dataargument is excluded fromfullmatchorpairmatchandnum_NA> 0 entries in the treatment status vector are NA, then the length of the vector produced byfullmatchorpairmatchwon't match the length of the treatment status vector, havingnum_NAfewer observations. Don't forget to pass adataargument! - Fixed bug affecting rank Mahalanobis matching in combination with calipers and/or exact matching constraints (#128)
- Addressed an issue affecting certain problems with both exact matching and caliper restrictions, where min/mean/max.controls directives would have been mistakenly applied to the wrong subclasses, resulting in strange warnings and, potentially, spurious match failures or unintended structural restrictions in some subclasses (#129).
- Structural restrictions allowing only many-one matches no longer cause fullmatch to automatically fail. I.e. we've restored the behaviour of the software prior to version 0.8. (#132)
CHANGES IN OPTMATCH VERSION 0.9-7
- Added support for CBPS created objects (#121).
- Improvments to documentation for several functions.
- Several small bugfixes.
CHANGES IN OPTMATCH VERSION 0.9-6
- New material in vignettes, on general use of the package and on import/export of matching results and material between R and SAS or Stata (Josh Errickson).
- New
summarymethods for InfinitySparseMatrix, BlockedInfinitySparseMatrix and DenseMatrix. I.e., you can callsummaryon the result of a call tomatch_onorcaliper. The information this returns may be useful for selecting caliper widths, and for managing computational burdens with large matching problems. - Streamlined combinations of exact and propensity score matching.
If you include "+ strata(fac)" on the right hand side of a propensity
scoring model formula, then pass the fitted model to
pairmatch(),fullmatch()ormatch_on, then the factor "fac" will both serve as an independent variable for the propensity model and an exact matching variable (#101). See the examples on the help documentation forfullmatch. pairmatchandfullmatchno longer generate "matched.distances" attributes for their results. To get this information, usematched.distances.- (Internal) methods for sorting of InfinitySparseMatrix's
- Deprecated: support passing the results of
fill.NAsdirectly toglmor similar. Use the traditional formula anddataargument version. See help documentation forfill.NAsfor examples. - Fixed: Rcpp incompatibilities for some OSX users (4bbcaca);
boxplotmethod for fitted propensities ignoring varwidth argument (#113); various minor issues affecting package development and deployment (#110,...).
CHANGES IN OPTMATCH VERSION 0.9-5
- Documentation adjustments.
- Explicit print method for output from explicit calls to
stratumStructure.
CHANGES IN OPTMATCH VERSION 0.9-4
- Significant speed up of math operations for sparse distance objects (by Josh Buckner).
- Introducing
contr.match_on, a new default contrasts function for making Mahalanobis and Euclidean distances. Previously we used R defaults, which (a) generated different answers for the same factor depending on the ordering of the levels and (b) led to different distances for {0,1}-valued numeric variables and two level factors. (#80) - match_on now takes strata as element of formula. Now users can write: match_on(z ~ x1 + x2 + strata(exactMatchVar)) Instead of match_on(z ~ x1 + x2, within = exactMatch(z ~ exactMatchVar))
- Fixed bug giving spurious infeasibility warnings, sample size
reductions when using
fullmatchwith feasible combinations ofmin.controls,mean.controls/max.controlsandmax.controls(#92) - Various small bug fixes and documentation improvements.
CHANGES IN OPTMATCH VERSION 0.9-3
- Fixed memory issues, potential segfaults in solver code. (Thank you, Peter Solenberger).
- Fixed bug in dropping cases with extraneous NAs when using
fullmatchorpairmatchto create distance specifications directly. - Fixed bug (#83) in
glmmethod formatch_onthat caused observations with fixable NAs to be dropped too often. - New function
distUnionallows combining arbitrary distance specifications. - New function
antiExactMatchprovides for matches that may only occur between treated and control units with different values on a factor variable. This is the opposite ofexactMatch, which ensures matches occur within factor levels. - Can now infer
dataargument in more cases when using thesummarymethod when the RItools package is present. - Additional warnings and clarifications.
CHANGES IN OPTMATCH VERSION 0.9-2
- Fixed issue #74 by properly setting the
omit.fractionargument when there are unmatched controls. - Improvements to the
minExactMatchfunction. - Added "optmatch_verbose_message" option to provide additional warnings.
- Fixed crash when all NULL or NA vectors passed as arguments to
fullmatch. - Added argument to
caliperfunction that allows returning values that fit the caliper instead of just indicators of which entries fit the caliper width. - Calipers widths can be given per-treated unit, instead of globally.
- Additional binary operators for sparse matrix representations.
- Added new ranked Mahalanobis method for the formula method of
match_on.
CHANGES IN OPTMATCH VERSION 0.9-1
- Subsetting of optmatch objects now preserves (and subsets) the subproblem attribute.
- Performance improvements for match_on applied to glm's.
- The solver update of version 0.9-0 had a bug that in some circumstances caused hangups or malloc's [Issue #70]. We believe this is now fixed -- but please notify maintainer if you continue to experience the problem. (If you do, we'll reward you with an easy workaround.)
CHANGES IN OPTMATCH VERSION 0.9-0
NEW FEATURES
-
Solver limits now depend on machine limits, not arbitrary constants defined by the optmatch maintainers. For large problems, users will see a warning, but the solver will attempt to solve.
-
fullmatch() and pairmatch() can now take distance generating arguments directly, instead of having to first call match_on(). See the documentation for these two functions for more details.
-
Infeasibility recovery in fullmatch(). When passing a combination of constraints (e.g. max.controls) that would make the matching infeasible, fullmatch() will now attempt to find a feasible match that respects those constraints, which will likely result in omitting some controls units.
-
An additional argument to fullmatch(), mean.controls, is an alternative to the previous omit.fraction. (Only one of the two arguments can be presented.) The match will attempt to average mean.controls number of controls per treatment.
-
Each optmatch object now carries with it the constraints used to generate it (e.g. max.controls) as well as a hashed version of the distance it matched up, to help with some debugging/error checking but avoiding having to carry the entire distance matrix around.
-
Creating a distance matrix prior to matching is now optional. fullmatch() now accepts arguments from which match_on() would create a distance, and create the match behind the scenes.
-
Performance enhancements for distance calculations.
-
Several new utility functions, including subdim(), optmatch_restrictions(), optmatch_same_distance(), num_eligible_matches(). See their help documentation for additional details.
-
Arithmetic operations between InfinitySparseMatrices and vectors are supported. The operation is carried out as column by vector steps.
-
scores() function allows including model predictions (such as propensity scores) in formulas directly (such as combining multiple propensity scores). The scores() function is preferred to predict() as it makes several smart choices to avoid dropping observations due to partial missingness and other useful preparations for matching.
BUG FIXES
-
match_on is now a S3 generic function, which solves several bugs using propensity models from other packages.
-
summary() method was giving overly pessimistic warnings about failures.
-
fixed bug in how optmatch objects were printing.
DEPRECATED AND DEFUNCT
- mdist() is now deprecated, in favor of match_on().
CHANGES IN OPTMATCH VERSION 0.8-3
- Changes to make examples compatible with PDF manual
CHANGES IN OPTMATCH VERSION 0.8-2
-
full() and pair() are now aliases to fullmatch() and pairmatch()
-
All match_on() methods take
caliperarguments (formerly just the numeric method and derived methods had this argument). -
boxplot methods for fitted propensity score methods (glm and bigglm)
-
fill.NAs now takes
contrasts.argargument to mimic model.matrix() -
Several bug fixes in examples, documentation
-
The methods pscore.dist() and mahal.dist() are now deprecated, with useful error messages pointing users to replacements.
-
Significant performance improvements for sparse matching problems.
-
Functions umatched() and matched() were backwards. Corrected.
CHANGES IN OPTMATCH VERSION 0.8-1
- Several small bug fixes
CHANGES IN OPTMATCH VERSION 0.8-0
NEW FEATURES
-
More efficient data structure for sparse matching problems, those with relatively few allowed (finite) distances between units. Sparse problems often arise when calipers are employed. The new data structure (
InfinitySparseMatrix) behaves like a simple matrix, allowingcbind,rbind, andsubsetoperations, making it easier to work with the olderoptmatch.dlistdata structure. -
match_on: A series of methods to generate matching problems using the new data structure when appropriate, or using a standard matrix when the problem is dense. This function is being deployed along side the
mdistfunction to provide complete backward compatibility. New development will focus on this function for distance creation, and users are encouraged to use it right away. One difference formdistusers is thewithinargument. This argument takes an existing distance specification and limits the new comparisons to only those pairs that have finite distances in thewithinargument. See thematch_on,exactMatch, andcaliperdocumentation for more details. -
exactMatch: A new function to create stratified matching problems (in which cross strata matches are forbidden). Users can specify the strata using either a factor vector or a convenient formula interface. The results can be used in calls
match_onto limit distance calculations to only with-in strata treatment-control pairs. -
New
dataargument tofullmatchandpairmatch: This argument will set the order of the match to that of therow.names,names, or contents of the passeddata.frameorvector. This avoids potential bugs caused when theoptmatchobjects were in a different order than users' data. -
Test suite expanded and now uses the testthat library.
-
fill.NAs allows (optionally) filling in all columns (previously, the first column was assumed to be an outcome or treatment indicator and was not filled in).
-
New tools to find minimum feasible constraints: Large matching problems could exceed the upper limit for a matching problem. The functions
minExactmatchandmaxCaliperfind the smallest interaction of potential factors for stratified matchings or the largest (most generous) caliper, respectively, that make the problem small enough to fit under the maximum problem size limit. See the help pages for these functions for more information.
BUG FIXES
- Unmatched units are always NA (instead of being labeled "1.NA" or similar).
This avoids some obscure bugs when feeding the results of
fullmatchto other functions.
FOR A DETAILED CHANGELOG, SEE https://github.com/markmfredrickson/optmatch
CHANGES IN OPTMATCH VERSION 0.7-1
NEW FEATURES
-
pairmatch() has a new option, "remove.unmatchables," that may be useful in conjunction with caliper matching. With "remove.unmatchables=TRUE", prior to matching any units with no counterparts within caliper distance are removed. Pair matching can still fail, if for example for two distinct treatment units only a single control, the same one, is available for matching to them; but remove.unmatchables eliminates one simple and common reason for pair matching to fail.
-
Applying summary() to an optmatch object now creates a "summary.optmatch" containing the summary information, in addition to reporting it to the console (via a summary.optmatch method for print() ).
-
mdist.formula() no longer requires an explicit data argument. I.e., you can get away with a call like "mdist(Treat~X1+X2|S)" if the variables Treat, X1, X2 and S are available in the environment you're working from (or in one of its parent environments). Previously you would have had to do "mdist(Treat~X1+X2|S, data=mydata)". (The latter formulation is still to be preferred, however, in part because with it mdist() gets to use data's row names, whereas otherwise it would have to make up row names.)
CHANGES IN OPTMATCH VERSION 0.7
NEW FEATURES
- New function fill.NAs replaces missing observations (ie. NA values) with minimally informative values (ie. the mean of observed columns). Fill.NAs handles functions in formulas intelligently and provides missing indicators for each variable. See the help documentation for more information and examples.
BUG FIXES
-
mdist.function method now properly returns an optmatch.dlist object for use in summary.optmatch, etc.
-
mdist.function maintains label on grouping factor.
CHANGES IN OPTMATCH VERSION 0.6-1
NEW FEATURES
-
New mdist method to extract propensity scores from models fitted using bigglm in package "biglm".
-
mdist's formula method now understands grouping factors indicated with a pipe ("|")
-
informative error message for mdist called on numeric vectors
-
updated mdist documentation
CHANGES IN OPTMATCH VERSION 0.6
NEW FEATURES
-
There is a new generic function, mdist(), for creating matching distances. It accepts: fitted glm's, which it uses to extract propensity distances; formulas, which it uses to construct squared Mahalanobis distances; and functions, with which a user can construct his or her own type of distance. The function method is more intuitive to work with than the older makedist() function.
-
A new function, caliper(), builds on the mdist() structure to provide a convenient way to add calipers to a distance. In contrast to earlier ways of adding calipers, caliper() has an optional argument specify observations to be excluded from the caliper requirement --- this permits one to relax it for just a few observations, for instance.
-
summary.optmatch() now removes strata in which matching failed (b/c the matching problem was found to be infeasible) before summarizing. It also indicates when such strata are present, and how many observations fall in them.
-
Demo has been updated to reflect changes as of version 0.4, 0.5, 0.6.
DEPRECATED & DEFUNCT
- The vignette is sufficiently out of date that it's been removed.
BUG FIXES
-
subsetting of objects of class optmatch now preserves matched.distances attribute.
-
fixed bug in maxControlsCap/minControlsCap whereby they behaved unreliably on subclasses within which some subjects had no permissible matches.
-
Removed unnecessary panic in fullmatch when it was given a min.controls argument with attributes other than names (as when it is created by tapply()).
-
fixed bug wherein summary.optmatch fails to retrieve balance tests if given a propensity model that had function calls in its formula.
-
Documentation pages for fullmatch, pairmatch filled out a bit.
CHANGES IN OPTMATCH VERSION 0.5
NEW FEATURES:
- summary.optmatch() completely revised. It now reports information about the configuration of the matched sets and about matched distances. In addition, if given a fitted propensity model as a second argument it summarizes covariate balance.