cobalt News and UpdatesMinor updates to bal.plot() to prevent warnings due to ggplot2 3.5.0.
Improved processing when no covariates are specified.
Fixed a bug when multiple weights are specified, s.d.denom is specified, and either all variables are continuous and continuous = "raw", all variables are binary and binary = "raw", or both continuous = "raw" and binary = "raw".
Documentation updates
Minor update to accommodate ggplot2 3.5.0. Thanks to @teunbrand. (#80)
bal.tab() no longer throws a note about s.d.denom when binary = "raw" and continuous = "raw" (i.e., only raw mean differences are requested).
col_w_smd() now correctly includes all data in computing the standardization factor for standardized mean differences when subset is supplied, consistent with the documentation. Previously, only the subsetted units were included in the standardization factor. This does not affect any results from bal.tab() or bal.compute(), which already used the correct units.
Added a new vignette for frequently asked questions, which describes in further detail why some choices were made. See vignette("faq").
Fixed a bug when missing values were present in continuous covariates. Thanks to @vnusinfo. (#76)
Fixed a bug when using bal.tab() with the cluster argument supplied with the caret package loaded. Thanks to @BorgeJorge. (#77)
When cluster is specified, categorical variables that perfectly coincide with the cluster variable are now correctly removed.
Perfectly colinear variables are no longer removed (unless they are binary variables split from the same factor). This should speed up evaluation and reduce the probability of false positives being removed.
Variables with a single value are now more reliably categorized as “binary” in tables and calculations.
Fixed a bug when using bal.compute() with a treatment variable with levels named “treated” and “control”.
Fixed a bug when using bal.tab() with mnps objects from twang. Thanks to @sherwinkuah. (#74)
Fixed a bug when using addl without a dataset supplied. (#71)
Fixed a bug when using subset to remove clusters lacking full representation in all treatment groups when cluster is specified. (#70)
Added a new function available.stats() which lists the available balance statistics for use with bal.init() and bal.compute().
The interfaces to bal.compute() and bal.init() have changed slightly. The arguments have a slightly different order to match other cobalt functions. bal.compute() now is a generic function with a method for bal.init objects and a default method. The default method accepts the same arguments as bal.init() (and optionally an additional weights argument) and computes the (weighted) balance statistic directly. For most uses, the bal.init() |> bal.compute() workflow should be preferred.
Added a new multivariate balance statistic, the kernel distance as described by Zhu, Savage, and Ghosh (2018). This can be requested in bal.compute() and bal.init() by setting stat = "kernel.dist". In most cases, this will perform similarly to the energy distance.
s.weights are more compatible with matching methods and can now be supplied to bal.tab() with mimids and wimids objects from MatchThem. Thanks to Helen Wright for pointing out this issue.
Fixed a bug when using bal.init() with non-NULL s.weights.
Fixed bugs when using bal.init() and bal.compute() with multi-category treatments.
Fixed a bug when using col_w_ovl() with missing data.
Fixed a bug when using bal.tab() with stat = "spearman.correlations".
Fixed a bug when using bal.plot() with longitudinal treatments.
Fixed a bug in while the display options factor_sep and int_sep were not functioning correctly.
Fixed a bug when no covariates are supplied.
Improved some errors all around, and particularly in col_w_smd() and friends, bal.init(), and var.names().
Added new functions bal.compute() and bal.init(), which are used for compute scalar balance statistics efficiently for use in optimizing balance. A new vignette, vignette("optimizing-balance") is available as well.
When focal is specified with multi-category treatments (by the user or implicitly by the supplied object), pairwise can be set to TRUE to request balance between each pair of treatment groups and to FALSE to request balance only between each non-focal group and the focal group. Previously only the behavior of setting pairwise to FALSE was supported. Now the default is for pairwise to be TRUE. To recover balance results for version prior to this one, set pairwise = FALSE with non-NULL focal.
With optmatch objects, the estimand argument can now be supplied to bal.tab(), etc., to control how the matching weights are computed from the subclass/pair membership. This is consistent with how get.w() uses the same argument.
Fixed a bug in which using . in formulas incorrectly included the treatment among the covariates.
Fixed a bug in which formulas supplied as character strings were not correctly interpreted as formulas. Thanks to @istallworthy.
Fixed a bug in which a spurious warning about dropping weights would occur when using bal.plot() with a density.
Documentation updates, including some new pages and the use of roxygen2.
Fixed a bug when covariates with nonstandard names are extracted from model objects (#63). Thanks to @markdanese.
Fixed a bug when “0” and “1” are the names of two of the treatment levels in a multinomial treatment.
Fixed a bug with the default method of bal.tab() which was ignoring components of the supplied object.
Fixed a bug where bal.plot() would ignore s.weights. They are now included correctly.
The call to the original balancing function is now hidden by default. To request it be displayed, set disp.call = TRUE in the call to bal.tab() or print.bal.tab() or use set.cobalt.options(disp.call = TRUE) to display it for the session.
Added support in bal.plot() for negative weights with type = "density".
Added support for ps.cont() objects from the twangContinuous package. ps.cont objects from WeightIt are no longer supported.
Major documentation overhaul. More arguments are explained at help("bal.tab") and a new package help page can be found at help("cobalt-package").
The function call is no longer included in the bal.tab() results for objects from twang.
Fixed a bug when some predictors were binary in some clusters and continuous in others. Variables now have a stable type across partitions.
Fixed a bug where binary variables were not being correctly processed when using the formula interface.
When using poly, orthogonal polynomials can be requested by setting orth = TRUE.
Improved appearance of conditional examples in pkgdown site.
Removed mlogit from Suggests.
Returned sbw to Suggests.
Updated the logo, thanks to Ben Stillerman.
When pairwise = FALSE with binary or multi-category treatments, the balance statistics now refer to the difference between each group and the original full sample, unadjusted except possibly by s.weights. Previously, they referred to the difference between each group and the combined adjusted sample.
When subclassification is used and some units are discarded, bal.tab() now reports the number of discarded units along with with the number of units in each subclass in the sample sizes table.(#59)
Fixed several bugs when using love.plot() with subclassification that were caused by the last update. Thanks to Mario Lawes for pointing them out.
Fixed a bug in how get.w() computed weights for Match objects resulting from Matching::Match() with estimand = "ATE". Results now agree with Matching::MatchBalance().
Fixed a bug that would occur when using cobalt functions without attaching the package (e.g., cobalt::bal.tab()). (#53)
Fixed a bug that would occur with ordinal treatments.
Added better support for negative weights.
Fixed typos (#54, many identified and fixed by @jessecambon).
Added support for objects from the new version of MatchThem.
Fixed a bug and improved speed when using match.strata.
Returned cem to Suggests.
Added ability to display threshold summaries with multiply imputed datasets, clustered datasets, multi-category treatments, and longitudinal treatments.
Added pairwise argument for binary treatments. When set to FALSE, bal.tab() will display balance between each treatment group and the full sample (i.e., the target population). This functionality already existed for multi-category treatments; indeed, for binary treatments, it works by treating the treatment as multi-category.
Added two new stats options in bal.tab() and love.plot() for continuous treatments: "mean.diffs.target" (abbreviated as "m") and "ks.statistics.target" (abbreviated as "ks"). These compute (standardized) mean differences and KS statistics between the weighted and unweighted samples to ensure the weighted sample is representative of the original population. These statistics are only computed for the adjusted sample (i.e., they will not appear in the absence of adjustment).
With subclassification methods, the arguments which.subclass and subclass.summary have been added to display balance on individual subclasses and control output of the balance across subclasses summary. These arguments replace the disp.subclass argument, which can still be used.
When using bal.plot() with clustered or multiply imputed data, the which.cluster and which.imp arguments can be set to .none to display balance ignoring cluster membership and combining across imputations.
Changed processing of the print() method. Now there is only one print() method (print.bal.tab()) for all bal.tab objects. Processing is a little smoother and some printing bugs have been fixed. "as.is" can no longer be supplied to keep the print setting as-is; simply omit the corresponding argument to use the options as specified in the call to bal.tab().
An additional argument, disp.call, can be supplied to bal.tab() and print.bal.tab() to control printing of the call component of the input object, which contains the original function call. Set to FALSE to hide the call. This option is documented in ?display_options and can also be set using set.cobalt.options().
The balance table component of bal.tab objects is smaller because some extraneous columns are no longer produced. In particular, if no threshold is requested, no threshold columns will be produced. This does not affect display, but makes it easier to extract balance statistics from bal.tab objects (e.g., for exporting as a table). This does mean that previously saved bal.tab objects produced by earlier versions of cobalt will not be able to be printed correctly.
Fixed a bug where bal.plot() would incorrectly process 2-level factor variables (#48).
Fixed a bug where love.plot() would not display variables in the correct order when using aggregation and setting var.order = NULL. Thanks to Florian Kaiser.
Fixed a bug in love.plot() where the color of points could be incorrect.
Fixed a bug in love.plot() where samples were not always displayed in the right order. Now they are displayed in the same order they are in bal.tab().
Fixed a bug in love.plot() when the weight names had spaces in them.
Added an error message when not all clusters contain all treatment levels. Thanks to Rachel Visontay.
Fixed a bug when supplying the weights argument as a list of supported objects (e.g., weightit objects) if they were unnamed. Samples are more conveniently named.
Fixed a bug in col_w_mean(), col_w_smd(), and friends that occurred when few nonzero weights were present. Now an informative error is thrown.
Updates to documentation.
Sampling weights now function correctly with subclassification.
Fixed a bug in print.bal.tab() when no units were unmatched but some were discarded.
Fixed an issue with the version number for gridExtra in DESCRIPTION. (#47)
cem removed from Suggests because it has been removed from CRAN.
Updated to support MatchIt 4.0.0, which includes sampling weights and improved processing of the covariates.
Fixed bugs in processing functions in formulas, including rms functions and poly(). (#40)
Fixed a bug in how KS statistics were computed with col_w_ks(). Results now agree with those from MatchIt and twang.
Fixed bugs in processing small and partially empty subclasses.
In functions that compute weights from matching strata (e.g., get.w() for some types of objects), an estimand argument can be supplied to choose which formula is used to compute the weights. Subclass propensity scores are computed as the number of treated units in each subclass, and then stabilized weights are computed from those propensity scores using the standard formulas.
Effective sample sizes now print only up to two digits (believe me, you don’t need three) and print more cleanly with whole numbers.
Fixed a bug due to new version of sbw.
Minor improvements to error messages and documentation.
Fixed a bug where int and poly were ignored with binary and continuous treatments.
Fixed a bug where subclass balance statistics were incorrectly computed. Thanks to Mario Lawes.
Improved processing of inappropriately given S4 objects.
Removed bal.tab methods for atomic vectors (which were undocumented). The errors they would provide when inappropriately supplied were unhelpful.
Fixed a bug with backports 1.1.7 not running correctly.
Fixed a bug with str2expression for R versions below 3.6.0. Thanks to @kthohr and @jimmyg909.
When data is segmented (i.e., with a multi-category or longitudinal treatment or when clusters or multiple imputations are specified), the balance summary across segments will not be computed or displayed when individual segment balance is requested. See ?display_options to see the defaults for the different segment types, some of which have changed.
Updated some warnings.
Added support for Matchby objects resulting from a call to Matchby() in the Matching package. These function identically to Match objects.
When using formula inputs, interaction terms (e.g., X1 * X2) will now correctly be resolved and displayed as an interaction term. This makes it easier to check balance on specific interactions rather than having to set int = TRUE or create a separate interaction variable in the data. Interaction terms specified in this way will be ignored by int and poly when they are used. When using var.names with love.plot(), changing the names of the base components of the interaction will also change their name in the interaction term, consistent with int behavior. If a formula was supplied in the input object to bal.tab() or other functions, terms in that formula will now also be included in balance reports.
Arguments to addl can now be specified as a one-sided formula (e.g., ~ X1 + X2 * X3). This makes it easy to take advantage of the above changes to the formula interface to add additional interaction terms. The formula will look at all available datasets in the conditioning object or supplied to bal.tab() and at the global environment. If supplying a single variable that exists in the global environment, it makes sense to supply it as a formula (e.g., addl = ~ X1) rather than as just the variable (e.g., addl = X1). Doing the former will retain the name of the variable. The same can be done with distance. If variables in addl are perfectly correlated with or have the same name as supplied covariates, those variables will be removed from addl.
If only one argument is provided to f.build() (e.g., f.build("x")), it will be treated as the right-hand-side of the formula with no left-hand-side (e.g., the above will evaluate to ~ x).
Fixed bug that caused match.strata input to be ignored.
Improved processing and error reporting when using the default bal.tab() method.
Speed improvements due to changes in how formulas are processed (now using model.matrix() directly rather than splitfactor() to process factors) and other small fixes. This is what enables the above changes to the formula capabilities.
Improved documentation for weightitMSM objects from WeightIt and CBMSM objects from CBPS.
Fixed bug when printing bal.tab objects with continuous treatments.
Fixed bug when using multi-category treatments with numbers as the level names.
Fixed bug when using mnps objects from twang with multiple stop methods.
Fixed bug when requesting means or standard deviations with segmented data.
Fixed bug where the x-axis in love.plot was always “Standardized Mean Differences” even when it wasn’t supposed to be for stats = "mean.diffs".
rlang is now in IMPORTS.
General speed and stability improvements.
Added support for sbwcau objects from sbw. See Appendix 1 or ?bal.tab.sbw for an example.
Added support for cem.match objects from cem. See Appendix 1 or ?bal.tab.cem.match for an example.
Added stats argument to bal.tab() and print() to replace disp.v.ratio and disp.ks. This argument functions similarly to how it does in love.plot(); for example, to request mean differences and variance ratios, one can enter stats = c("m", "v"). One consequence of this is that it is possible to request statistics that don’t include mean differences. See ?display_options for more details. The old arguments still work (and probably always will) but you should use stats instead. The goal here was to unify syntax across bal.tab(), print(), and love.plot(). A new help page specifically for the stats argument can be viewed at ?balance.stats.
Added thresholds argument to bal.tab() to replace m.threshold, v.threshold, etc. This argument functions similarly to how it does in love.plot(); for example, to request thresholds for mean differences and variance ratios, one can enter thresholds = c(m = .1, v = 2). The old arguments still work (and probably always will) but you should use thresholds instead. The goal here was to unify syntax across bal.tab(), print(), and love.plot().
Added disp.means option to bal.plot to display the mean of the covariate as a line on density plots and histograms.
Added "hedges" as an option to s.d.denom. This will compute the standardized mean difference using the formula for the small sample-corrected Hedge’s G as described in the What Works Clearinghouse Procedures Handbook.
With multi-category treatments when pairwise = FALSE, rather than computing balance between each treatment group and the other treatment groups, balance is now computed between each treatment group and the entire sample.
In print(), the arguments disp.m.threshold, disp.v.threshold, disp.ks.threshold, and disp.r.threshold, which could be set to FALSE to prevent the corresponding balance thresholds and summaries from being printed, have been replaced with disp.thresholds. Named entries can be set to FALSE. The goal here was to unify syntax across bal.tab() and print().
A new balance statistic, the overlapping coefficient (OVL), is allowed with binary and multi-category treatments. This is described in Belitser et al. (2011) and Franklin et al. (2014) for assessing balance. Generally, for each covariate, the overlapping coefficient is the area of the probability density functions for each sample that overlap. Here I follow Franklin et al. (2014) and report 1 - (OVL) so that values close to zero indicate good balance (i.e., completely overlapping distributions) and values close to 1 indicate poor balance (i.e., completely non-overlapping distributions). To estimate and display the OVL, set include "ovl" in the stats argument in a call to bal.tab() or love.plot() (or you can use the old syntax by setting disp.ovl = TRUE). The balance threshold can be requested by including "ovl" in the thresholds argument (or you can use the old syntax by using the ovl.threshold argument).
Spearman correlations can be requested for continuous treatments by adding "sp" to the stats argument.
The argument weights can now be supplied to any bal.tab() call to request balance on additional weights beyond the weights from the object on which bal.tab() is called. This argument takes a named list, where each element is a vector of weights, the name of a variable containing weights in an available dataset, or an object with a get.w() method (e.g., the output of another preprocessing function). This should make it easier to compare balancing methods without having to specify the covariates and treatment using the formula or data.frame methods.
ggplot2 version 3.3.0 is required, which removes some warnings and makes it so ggstance doesn’t need to be imported.
When there are more than 900 variables to compute balance statistics on in bal.tab (which can happen quickly when int = TRUE and categorical variables have many categories), to avoid major slowdowns, checks for redundancy of variables are forgone. This will dramatically increase the speed of bal.tab in these scenarios. This option can be changed with the cobalt option "remove_perfect_col" which can be set to TRUE or or FALSE. Set to FALSE to improve speed at the expense of possibly having redundant variables appear.
Fixed a bug when using the default bal.tab method with objects containing longitudinal treatments.
Fixed a bug when using bal.tab with continuous treatments and clusters.
Fixed a bug in love.plot() when using subclassification.
Fixed a bug when using bal.tab with longitudinal treatments and multiple sets of weights.
Fixed a bug when using col_w_ovl(). OVL values are now more accurate.
Speedups and other small fixes.
Major Updates
Added support for mimids and wimids objects from MatchThem.
Major restructuring so that clusters, longitudinal treatments, multi-category treatments, and multiply imputed data can all be used with each other. These are layers in the following order: clusters, time points, treatment categories, and imputations. Summaries across these layers are handled slightly differently from how they used to be; importantly, summaries are not nested, and only the lowest layer present can have a summary. For example, if multiply imputed data is used with multi-category treatments, there will be a summary across imputations (the lowest layer) but not across treatment pairs. love.plot allows multiple forms of faceting and aggregating and is extremely flexible in this regard.
Major changes to appearance of bal.plot() to be more in line with love.plot(), including new grid and position options to control the presence of the grid and the position of the legend.
Formula interfaces now accept poly(x, .) and other matrix-generating functions of variables, including the rms-class-generating functions from the rms package (e.g., pol(), rcs(), etc.) (the rms package must be loaded to use these latter ones) and the basis-class-generating functions from the splines package (i.e., bs() and ns()). A bug in an early version of this was found by @ahinton-mmc.
Minor Updates and Bug Fixes
s.d.denom and estimand
s.d.denom can now use the name of a treatment rather than just "treated" or "control". In addition, s.d.denom can be "weighted" to use the weighted sample’s standardization factors, an option available for continuous treatments, too.
Improved guessing of the estimand when not provided.
Estimands besides ATT can now be used with subclasses. The estimand can be inferred from the provided subclasses. Works with match.strata as well, which function like subclasses. In addition, it is not always assumed that MatchIt objects are targeting the ATT, for example, with subclassification or calipers.
bal.plot
Added sample.names argument in bal.plot in response to this post on Cross Validated.
Added functionality to the which argument in bal.plot, allowing more specificity when multiple sets of weights are used.
Added type = "ecdf" option to bal.plot for categorical treatments with continuous covariates to display empirical cumulative density plots as an alternative to density plots.
When using bal.plot with continuous treatments and continuous covariates, the points are shaded based on their weights; this behavior is controlled by the new alpha.weight argument, which replaces the functionality of size.weight (which was kind of ugly and not very informative) and is TRUE by default. Now it’s more apparent which points are influential in the weighted sample. In addition, a line illustrating the unweighted covariate mean is present.
The default of the grid argument is now FALSE in bal.plot() and love.plot(). Previously it was TRUE. This make the plots cleaner at the outset.
Other improvements
Added new function col_w_cov() to compute treatment-covariate covariances (i.e., unstandardized correlations) for continuous treatments. continuous and binary can be set to "raw" in bal.tab() and std can be set to FALSE in col_w_cov() to request treatment-covariate covariances instead of correlations. col_w_corr() is now a wrapper for col_w_cov() with std = TRUE. To get more functionality out of the std argument (e.g., to standardize the covariances for some covariates but not others), use col_w_cov().
Balance summary functions (e.g., col_w_sd(), col_w_smd(), etc.) process binary variables slightly differently. If bin.vars is missing, the function will figure out which variables are binary. If NULL, it will be assumed no variables are binary. Entering values for bin.vars can be done more flexibly. When a factor variable is supplied as part of mat and is split internally by splitfactor(), extra values will be automatically added to bin.vars with the newly created dummies considered binary variables.
Bug fixes when binary factor treatments are used, thanks to Moaath Mustafa Ali.
bal.tab() no longer tells you whether it assumes matching or weighting when certain non-package-related methods are used.
Improvements to assessment of subclass balance. For binary treatments, balance statistics other than mean differences can now be requested. The across-subclass balance summary uses subclassification weights (processed in the same way match.strata is) instead of simply taking a weighted average across subclasses (which is not valid for non-additive statistics like variance ratios or KS statistics). For continuous treatments, a balance summary across subclasses can now be produced. This uses a weighted average of the subclass-specific balance statistics.
The default in love.plot() for abs is now to be whatever it is in the (implicit) call to bal.tab(), which is usually FALSE. Previously abs was not aligned between love.plot() and bal.tab().
s.weights can now be manually supplied to methods that usually come with their own sampling weights, such as twang and WeightIt.
Speedup of splitfactor().
splitfactor() now has a split.with option to split one or more vectors in concert with the data set being split.
splitfactor() and unsplitfactor() are a little smarter and more in sync.
All functions work better inside other functions like lapply() or purrr::map(), thanks to @the-Zian.
Updates to the vignettes; Appendix 2 is particularly different.
Other bug fixes and performance improvements here and there.
Added vignette for use of love.plot.
Changed grid version requirement.
Updated README.
Fixed bugs that would occur when using love.plot() with various combinations of var.order, multiple stats, and agg.fun = "range".
Fixed bugs that would occur when using bal.tab() with objects from the Matching package. Calculated statistics are now the same as those generated using Matching::MatchBalance. Changes based on updates to get.w.Match().
Added balance summary functions col_w_mean, col_w_sd, col_w_smd, col_w_vr, col_w_ks, col_w_ovl, and col_w_corr. These make it easier to get quick, simple summaries of balance without calling bal.tab, for example, for use in programming other functions. Some of these are now used inside bal.tab to increase speed and simplify internal syntax.
Other small bug fixes.
Added the ability to display balance on multiple measures (e.g., mean differences, variance ratios, KS statistics) at the same time with love.plot().
Bug fixes that make bal.tab() and love.plot() more usable within other functions and especially when called with do.call().
Made it easier to get proper bal.tab output when using matchit() with an argument to distance (in the call to matchit()). Include the original dataset in the data argument of bal.tab() to get the variables to display correctly.
Changed the default shape in love.plot() to "circle", which is a solid circle. I found this a prettier alternative to the open circle, especially on Windows. To get back open circles you set shapes = "circle filled" (yes, that is a bit confusing).
Added ability to hide the gridlines easily in love.plot().
Changed the calculation of standard deviations (and standardized differences in proportion) for binary variables to be more in line with recommendations, as noted by @mbloechl05. Note this will make these values different from those in MatchIt::summary by a small amount.
The KS statistic is now computed for binary variables. It is simply the difference in proportion.
Allowed some methods to accept mids objects (the output of a call to mice::mice()) in the data argument to supply multiply imputed data. This essentially replaces data = complete(imp.out, "long"), imp = ".imp" with data = imp.put, assuming imp.out is a mids object.
Other bug fixes and improvements.
Changes to some bal.tab defaults: quick is now set to TRUE by default. Adjusted and unadjusted means, standard deviations, and mean differences will always be computed, regardless of quick. Variance ratios and KS statistics will only be computed if quick = FALSE or disp.v.ratio or disp.ks, respectively, are TRUE.
Variance ratios now respond to abs. When abs = FALSE, the default in bal.tab, the variance is ratio is the variance of the treated (1) divided by the variance of the control (0). When abs = TRUE, the numerator of the variance ratio is the larger variance and the denominator is the smaller variance, which was the old behavior. v.threshold still responds as if abs was set to TRUE, just like with mean differences. Any time variance ratios are aggregated (e.g., across imputations or clusters), the “mean” variance ratio is the geometric mean to account for the asymmetry in the ratios.
love.plot has several changes that make it much more user-friendly. First, rather than supplying a bal.tab object to love.plot, you can simply supply the arguments that would have gone into the bal.tab() call straight into love.plot(). Second, if quick = TRUE (the new default) and the first argument to love.plot() is a call to bal.tab() (or arguments provided to bal.tab()) and stat is set to "variance.ratios" or "ks.statistics", bal.tab() will be re-called with the corresponding disp argument set to TRUE so that love.plot() will display those statistics regardless of quick. This will not work if the argument supplied to love.plot() is a bal.tab object. Third, because unadjusted mean differences are computed regardless of quick, there will never be a circumstance in which only adjusted values will be displayed. If quick = TRUE, un = FALSE, and stat is "variance.ratios" or "ks.statistics", un will automatically be set to TRUE in the bal.tab() re-call.
When using which. arguments (e.g., which.cluster, which.imp, etc.), instead of supplying NULL and NA, you can supply .all and .none (not in quotes). This should make them easier to use. Note that these new inputs are not variables; they are keywords and are evaluated using nonstandard evaluation. If you actually have objects with those names, they will be ignored.
Bugs in scoping related to the formula interface have been solved, in particularly making bal.tab() more usable within other functions.
Fixed bug occurring when using matchit objects having set discard to something other than NULL and reestimate = TRUE in the call to matchit(). Thank you to Weiyi Xie for finding this bug.
Fixed bug occurring when using balance thresholds with subclassification.
Fixed bug occurring when printing bal.tab output for continuous treatments with clusters.
Fixed bug occurring when using bal.tab() on mnps objects with multiple stop methods.
Added poly argument to bal.tab() to display polynomials of continuous covariates (e.g., squares, cubes, etc.). This used to only be available with the int argument, which also displayed all interactions. Now, the polynomials can be requested separately. When int = TRUE, squares of the covariates will no longer be displayed; to replicate the old behavior, set int = 2, which is equivalent to int = TRUE, poly = 2.
Fixed a bug where using subset would produce an error.
Fixed a bug when using multiply imputed data with binary treatments that were factors or characters.
Updated the bal.tab documentation to make it easier to navigate to the right page.
Small documentation and syntax updates.
Added the hidden and undocumented argument center to bal.tab, which, when set to TRUE, centers the covariates at the mean of the entire unadjusted sample prior to computing interactions and polynomials.
Added set.cobalt.options function to more easily set the global options that can be used as defaults to some arguments. For example, set.global.options(binary = "std") makes it so that standardized mean difference are always displayed for binary covariates (in the present R session). The options can be retrieved with get.cobalt.options.
Several changes to bal.tab() display options (i.e., imbalanced.only, un, disp.means, disp.v.ratio, disp.ks, disp.bal.tab, disp.subclass, and parameters related to the display of balance tables with multinomial treatments, clusters, multiple imputations, and longitudinal treatments). First, the named arguments have been removed from the method-specific functions in order to clean them up and make it easier to add new functions, but they are still available to be specified. Second, a help page devoted just to these functions has been created, which can be accessed with ?options-display. Third, global options for these arguments can be set with options() so they don’t need to be typed each time. For example, if you wanted un = TRUE all the time, you could set options(cobalt_un = TRUE) once and not have to include it in the call to bal.tab().
Added disp.sds option to display standard deviations for each group in bal.tab(). This works in all the same places disp.means does.
Added cluster.fun and imp.fun options to request that only certain functions (e.g., mean or maximum) of the balance statistics are displayed in the summary across clusters/imputations. Previously this option was only available by call print(). These parameters are part of the display options described above, so they are documented in ?options-display and not in the bal.tab help files.
Added factor_sep and int_sep options to change the separators between variable names when factor variables and interactions are displayed. This functionality had been available since version 3.4.0 but was not documented. It is now documented in the new display_options help page.
In bal.tab(), continuous and binary can be specified with the global options "cobalt_continuous" and "cobalt_binary", respectively, so that a global setting (e.g., to set binary = "std" to view standardized mean difference rather than raw differences in proportion for binary variables) can be used instead of specifying the argument each time in the call to bal.tab().
Minor updates to f.build() to process inputs more flexibly. The left hand side can now be empty, and the variables on the right hand side can now contain spaces.
Fixed a bug when logical treatments were used. Thanks to @victorn1.
Fixed a bug that would occur when a variable had only one value. Thanks to @victorn1.
Made it so the names of 0/1 and logical variables are not printed with "_1" appended to them. Thanks to @victorn1 for the suggestion.
Major updates to the organization of the code and help files. Certain functions have simplified syntax, relying more on ..., and help pages have been shorted and consolidated for some methods. In particular, the code and help documents for the Matching, optmatch, ebal, and designmatch methods of bal.tab() have been consolidated since they all rely on exactly the same syntax.
Fixed a bug that would occur when imabalanced.only = TRUE in bal.tab() but all variables were balanced.
Fixed a bug where the mean of a binary variable would be displayed as 1 minus its mean.
Fixed a bug that would occur when missingness patterns were the same for multiple variables.
Fixed a bug that would occur when a distance measure was to be assessed with bal.tab() and there were missing values in the covariates (thanks to Laura Helmkamp).
Fixed a bug that would occur when estimand was supplied by the user when using the default method of bal.tab().
Fixed a bug where non-standard variable names (like "I(age^2)") would cause an error.
Fixed a bug where treatment levels that had different numbers of characters would yield an error.
Added disp.means option to bal.tab with continuous treatments.
Added default method for bal.tab so it can be used with specially formatted output from other packages (e.g., from optweight). bal.plot should work with these outputs too. This, of course, will never be completely bug-free because infinite inputs are possible and cannot all be processed perfectly. Don’t try to break this function :)
Fixed some bugs occurring when standardized mean differences are not finite, thanks to Noémie Kiefer.
Speed improvements in bal.plot, especially with multiple facets, and in bal.tab.
Added new options to bal.plot, including the ability to display histograms rather than densities and mirrored rather than overlapping plots. This makes it possible to make the popular mirrored histogram plot for propensity scores. In addition, it’s now easier to change the colors of the components of the plots.
Made behavior around binary variables with interactions more like documentation, where interactions with both levels of the variable are present (thanks to @victorn1). Also, replaced _ with * as the delimiter between variable names in interactions. For the old behavior, use int_sep = "_" in bal.tab.
Expanded the flexibility of var.names in love.plot so that replacing the name of a variable will replace it everywhere it appears, including interactions. Thanks to @victorn1 for the suggestion.
Added var.names function to extract and save variable names from bal.tab objects. This makes it a lot easier to create replacement names for use in love.plot. Thanks to @victorn1 for the suggestion.
When weighted correlations are computed for continuous treatments, the denominator of the correlation now uses the unweighted standard deviations. See ?bal.tab for the rationale.
Added methods for objects from the designmatch package.
Added methods for ps.cont objects from the WeightIt package.
Fixed bugs resulting form changes to how formula inputs are handled.
Cleaned up some internal functions, also fixing some related bugs
Added subset option in all bal.tab() methods (and consequently in bal.plot()) that allows users to specify a subset of the data to assess balance on (i.e., instead of the whole data set). This provides a workaround for methods were the cluster option isn’t allowed (e.g., longitudinal treatments) but balance is desired on subsets of the data. However, in most cases, cluster with which.cluster specified makes more sense.
Updated help files, in particular, more clearly documenting methods for iptw objects from twang and CBMSM objects from CBPS.
Added pretty printing with crayon, inspired by Jacob Long’s jtools package
Added abs option to bal.tab to display absolute values of statistics, which can be especially helpful for aggregated output. This also affects how love.plot() handles aggregated balance statistics.
Added support for data with missing covariates. bal.tab() will produce balance statistics for the non-missing values and will automatically create a new variable indicating whether the variable is missing or not and produce balance statistics on this variable as well.
Fixed a bug when displaying maximum imbalances with subclassification.
Fixed a bug where the unadjusted statistics were not displayed when using love.plot() with subclasses. (Thanks to Megha Joshi.)
Add the ability to display individual subclass balance using love.plot() with subclasses.
Under-the-hood changes to how weightit objects are handled.
Objects in the environment are now handled better by bal.tab() with the formula interface. The data argument is now optional if all variables in the formula exist in the environment.
Fixed a bug when using get.w() (and bal.tab()) with mnps objects from twang with only one stop method.
Fixed a bug when using bal.tab() with twang objects that contained missing covariate values.
Fixed a bug when using int = TRUE in bal.tab() with few covariates.
Fixed a bug when variable names had special characters.
Added ability to check higher order polynomials by setting int to a number.
Changed behavior of bal.tab() with multinomial treatments and s.d.denom = "pooled" to use the pooled standard deviation from the entire sample, not just the paired treatments.
Restored some vignettes that required WeightIt.
Edits to vignettes and help files to respond to missing packages. Some vignette items may not display if packages are (temporarily) unavailable.
Fixed issue with sampling weights in CBPS objects. (Thanks to @kkranker on Github.)
Added more support for sampling weights in get.w() and help files.
Added support for longitudinal treatments in bal.tab(), bal.plot(), and love.plot(), including output from iptw() in twang, CBMSM() from CBPS, and weightitMSM() from WeightIt.
Added a vignette to explain use with longitudinal treatments.
Edits to help files.
Added ability to change density options in bal.plot().
Added support for imp in bal.tab() for weightit objects.
Fixed bugs when limited variables were present. (One found and fixed by @sumtxt on Github.)
Fixed bug with multiple methods when weights were entered as a list.
Added full support for tibbles.
Examples for weightit methods in documentation and vignette now work.
Improved speed and performance.
Added pairwise option for bal.tab() with multinomial treatments.
Increased flexibility for displaying balance using love.plot() with clustered or multiply imputed data.
Added imbalanced.only and disp.bal.tab options to bal.tab().
Fixes to the vignettes. Also, creation of a new vignette to simplify the main one.
Added support for multinomial treatments in bal.tab(), including output from CBPS and twang.
Added support for weightit objects from WeightIt, including for multinomial treatments.
Added support for ebalance.trim objects from ebal.
Fixes to the vignette.
Fixes to splitfactor() to handle tibbles better.
Fixed bug when using bal.tab() with multiply imputed data without adjustment. Fixed bug when using s.weights with the formula method of bal.tab().
Added disp.ks and ks.threshold options to bal.tab() to display Kolmogorov-Smirnov statistics before and after preprocessing.
Added support for sampling weights, which are applied to both control and treated units, using option s.weights in bal.tab(). Sampling weights are also now compatible with the sampling weights in ps objects from twang; the default is to apply the sampling weights before and after adjustment, mimicking the behavior of bal.table() in twang.
Changed behavior of bal.tab() for ps objects to allow displaying balance for more than one stop method at a time, and to default to displaying balance for all available stop methods. The full.stop.method argument in bal.tab() has been renamed stop.method, but full.stop.method still works. get.w() for ps objects has also gone through some changes to be more like twang’s get.weights().
Added support in bal.tab() and bal.plot() for subclassification with continuous treatments.
Added support in splitfactor() and unsplitfactor() for NA values
Fixed a bug in love.plot() caused when var.order was specified to be a sample that was not present.
Added support in bal.tab(), bal.plot(), and love.plot() for examining balance on multiple weight specifications at a time
Added new utilities splitfactor(), unsplitfactor(), and get.w()
Added option in bal.plot() to display points sized by weights when treatment and covariate are continuous
Added which = "both" option in bal.plot() to simultaneously display plots for both adjusted and unadjusted samples; changed argument syntax to accommodate
Allowed bal.plot() to display balance for multiple clusters and imputations simultaneously
Allowed bal.plot() to display balance for multiple subclasses simultaneously with which.sub
Fixes to love.plot() to ensure adjusted points are in front of unadjusted points; changed colors and shape defaults and allowable values
Fixed bug where s.d.denom and estimand were not functioning correctly in bal.tab()
distance, addl, and weights can now be specified as lists of the usual arguments
Added support for matching using the optmatch package or by specifying matching strata.
Added full support (bal.tab(), love.plot(), and bal.plot()) for multiply imputed data, including for clustered data sets.
Added support for multiple distance measures, including special treatment in love.plot()
Adjusted specifications in love.plot() for color and shape of points, and added option to generate a line connecting the points.
Adjusted love.plot() display to perform better on Windows.
Added capabilities for love.plot() and bal.plot() to display plots for multiple groups at a time
Added flexibility to f.build().
Updated bal.plot(), giving the capability to view multiple plots for subclassified or clustered data. Multinomial treatments are also supported.
Created a new vignette for clustered and multiply imputed data
Speed improvements
Fixed a bug causing mislabelling of categorical variables
Changed calculation of weighted variance to be in line with recommendations; CBPS can now be used with standardized weights
Added support for entropy balancing through the ebal package.
Changed default color scheme of love.plot() to be black and white and added options for color, shape, and size of points.
Added sample size calculations for continuous treatments.
Edits to the vignette.
Increased capabilities for cluster balance in bal.tab() and love.plot()
Increased information and decreased redundancy when assessing balance on interactions
Added quick option for bal.tab() to increase speed
Added options for print()
Bug fixes
Speed improvements
Edits to the vignette
Added support for continuous treatment variables in bal.tab(), bal.plot(), and love.plot()
Added balance assessment within and across clusters
Other small performance changes to minimize errors and be more intuitive
Major revisions and adjustments to the vignette
Added a vignette.
Fixed error in bal.tab.Match() that caused wrong values and and warning messages when used.
Added new capabilities to bal.plot(), including the ability to view unadjusted sample distributions, categorical variables as such, and the distance measure. Also updated documentation to reflect these changes and make which.sub more focal.
Allowed subclasses to be different from simply 1:S by treating them like factors once input is numerical
Changed column names in Balance table output to fit more compactly, and updated documentation to reflect these changes.
Other small performance changes to minimize errors and be more intuitive.