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predict. (#223)@call slot of objects may look slighly different (but should function identically). (#234)bibentry().Minor revision to address a failing test.
match_on(), now more simply calculates the contrast to enable more intuitive results. (Thanks Noah Greifer, #220)dbind() will now properly support binding more than 26 unique matrices when renaming is necessary; in fact it supports up to 18,278 uniquely renamed matrices.match_on() using the argument method = "rank_mahalanobis" was accidentally returning the squared distance rather than the distance. This has been fixed. To recover results using the squared distance, square the results, e.g.: match_on(..., method = "rank_mahalanobis")^2. (Thanks Noah Greifer #218)as.list.BlockedInfinitySparseMatrix() to split a single BlockedInfinitySparseMatrix into a list of InfinitySparseMatrix based upon the separate blocks. (Called via as.list(b) when b is a BlockedInfinitySparseMatrix.)dbind() for binding several distance matrices into a single BlockedInfinitySparseMatrix. Valid inputs include any distance convertible into an InfinitySparseMatrix, or BlockedInfinitySparseMatrix, or lists of these. (#65)License_is_FOSS and License_restricts_use flags after 0.10.0 transition to an open license.optmatch::strata to be used in place of survival::strata. Loading survival and masking strata should not cause issues either.(Note: 0.10.1 and 0.10.2 were functionally equivalent releases updated to address an issue with CRAN and the License_is_FOSS and License_restricts_use flags.)
help(fullmatch) for a discussion on those, and the new argument to fullmatch(), solver =.survey::svyglm() (#194)survey::mad and survey::med interfacewithin= arguments to match_on(), or functions calling match_on() such as pairmatch() or fullmatch(), were sometimes ignored (#181).fullmatch() or pairmatch() found it infeasible to create matches within an exact matching category, under some circumstances all members of that category were being placed into a single category labeled 1.NA, or 2.NA etc. Instead, all members of that category are now NA (#203).match_on(), scores() to misinterpret propensity or other scores fitted with survey::svyglm() (#194).boxplot() gains a method for svyglm objects, e.g. propensity score models fitted with case weights via survey::svyglm() (#194).match_on.glm()’s arguments has changed slightly: to circumvent scale standardization when matching on a propensity score or other index, you should now pass standardization.scale = 1, not standardization.scale = NULL (#194).summary.optmatch() to fail b/c of NAs in the treatment variable (#155).exclude argument to match_on() mirroring the exclude argument for caliper().Optmatch objects now support an update() function, update.Optmatch(). (#54)Optmatch objects can be combined via a c() function, c.Optmatch(). (#68)labelled treatment vectors which often arise when importing from Stata or SPSS. (#159)matchfailed(). (#175)summary.optmatch().if(vectorOfThings) usage that will give an error in upcoming R release.controls times the number of treatments, it now attempts to match in that stratum by leaving out some of the treatment units. (#116)treatment_new = treatment == "T".data argument is excluded from fullmatch() or pairmatch() and num_NA > 0 entries in the treatment status vector are NA, then the length of the vector produced by fullmatch() or pairmatch() won’t match the length of the treatment status vector, having num_NA fewer observations. Don’t forget to pass a data argument!min.controls/mean.controls/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).fullmatch() to automatically fail. I.e. we’ve restored the behaviour of the software prior to version 0.8. (#132)summary() methods for InfinitySparseMatrix (summary.InfinitySparseMatrix()), BlockedInfinitySparseMatrix, (summary.BlockedInfinitySparseMatrix()) and DenseMatrix (summary.DenseMatrix()). I.e., you can call summary() on the result of a call to match_on() or caliper(). The information this returns may be useful for selecting caliper widths, and for managing computational burdens with large matching problems.pairmatch(), fullmatch() or match_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 for fullmatch().pairmatch() and fullmatch() no longer generate “matched.distances” attributes for their results. To get this information, use matched.distances().fill.NAs() directly to glm() or similar. Use the traditional formula and data argument version. See help documentation for fill.NAs() for examples.boxplot() method for fitted propensities ignoring varwidth argument (#113); various minor issues affecting package development and deployment (#110,…).stratumStructure().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)fullmatch() with feasible combinations of min.controls, mean.controls/max.controls and max.controls (#92)fullmatch() or pairmatch() to create distance specifications directly.glm() method for match_on() that caused observations with fixable NAs to be dropped too often.distUnion() allows combining arbitrary distance specifications.antiExactMatch() provides for matches that may only occur between treated and control units with different values on a factor variable. This is the opposite of exactMatch(), which ensures matches occur within factor levels.data argument in more cases when using the summary() method when the RItools package is present.omit.fraction argument when there are unmatched controls.minExactMatch() function.optmatch_verbose_message option to provide additional warnings.fullmatch().caliper() function that allows returning values that fit the caliper instead of just indicators of which entries fit the caliper width.match_on().Optmatch objects now preserves (and subsets) the subproblem attribute.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.
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.
mdist() is now deprecated, in favor of match_on().full() and pair() are now aliases to fullmatch() and pairmatch()
All match_on() methods take caliper arguments (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.arg argument 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.
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, allowing cbind(), rbind(), and subset() operations, making it easier to work with the older optmatch.dlist data 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 mdist() function 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 for mdist() users is the within argument. This argument takes an existing distance specification and limits the new comparisons to only those pairs that have finite distances in the within argument. See the match_on(), exactMatch(), and caliper() documentation 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_on() to limit distance calculations to only with-in strata treatment-control pairs.
New data argument to fullmatch() and pairmatch(): This argument will set the order of the match to that of the row.names, names, or contents of the passed data.frame or vector. This avoids potential bugs caused when the optmatch objects 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 minExactmatch() and maxCaliper() find 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.
1.NA or similar). This avoids some obscure bugs when feeding the results of fullmatch() to other functions.FOR A DETAILED CHANGELOG, SEE https://github.com/markmfredrickson/optmatch
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.)
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.mdist.function() method now properly returns an optmatch.dlist object for use in summary.optmatch(), etc.
mdist.function() maintains label on grouping factor.
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
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.
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.
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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.