Generates balance statistics using an object for which there is not a defined method.

# S3 method for default
bal.tab(x,
        stats,
        int = FALSE,
        poly = 1,
        distance = NULL,
        addl = NULL,
        data = NULL,
        continuous,
        binary,
        s.d.denom,
        thresholds = NULL,
        weights = NULL,
        cluster = NULL,
        imp = NULL,
        pairwise = TRUE,
        s.weights = NULL,
        abs = FALSE,
        subset = NULL,
        quick = TRUE,
        ...)

Arguments

x

An object containing information about conditioning. See Details.

stats, int, poly, distance, addl, data, continuous, binary, s.d.denom, thresholds, weights, cluster, imp, pairwise, s.weights, abs, subset, quick

see bal.tab() for details.

...

other arguments that would be passed to bal.tab.formula(), bal.tab.data.frame(), or bal.tab.time.list(). See Details.

Details

bal.tab.default() processes its input and attempt to extract enough information from it to display covariate balance for x. The purpose of this method is to allow users who have created their own objects containing conditioning information (i.e., weights, subclasses, treatments, covariates, etc.) to access the capabilities of bal.tab() without having a special method written for them. By including the correct items in x, bal.tab.default() can present balance tables as if the input was the output of one of the specifically supported packages (e.g., MatchIt, twang, etc.).

The function will search x for the following named items and attempt to process them:

treat

A vector (numeric, character, factor) containing the values of the treatment for each unit or the name of the column in data containing them. Essentially the same input to treat in bal.tab.data.frame().

treat.list

A list of vectors (numeric, character, factor) containing, for each time point, the values of the treatment for each unit or the name of the column in data containing them. Essentially the same input to treat.list in bal.tab.time.list().

covs

A data.frame containing the values of the covariates for each unit. Essentially the same input to covs in bal.tab.data.frame().

covs.list

A list of data.frames containing, for each time point, the values of the covariates for each unit. Essentially the same input to covs.list in bal.tab.time.list().

formula

A formula with the treatment variable as the response and the covariates for which balance is to be assessed as the terms. Essentially the same input to formula in bal.tab.formula().

formula.list

A list of formulas with, for each time point, the treatment variable as the response and the covariates for which balance is to be assessed as the terms. Essentially the same input to formula.list in bal.tab.time.list().

data

A data.frame containing variables with the names used in other arguments and components (e.g., formula, weights, etc.). Essentially the same input to data in bal.tab.formula(), bal.tab.data.frame(), or bal.tab.time.list().

weights

A vector, list, or data.frame containing weights for each unit or a string containing the names of the weights variables in data. Essentially the same input to weights in bal.tab.data.frame() or bal.tab.time.list().

distance

A vector, formula, or data frame containing distance values (e.g., propensity scores) or a character vector containing their names. If a formula or variable names are specified, bal.tab() will look in the argument to data, if specified. Essentially the same input to distance in bal.tab.data.frame().

formula.list

A list of vectors or data.frames containing, for each time point, distance values (e.g., propensity scores) for each unit or a string containing the name of the distance variable in data. Essentially the same input to distance.list in bal.tab.time.list().

subclass

A vector containing subclass membership for each unit or a string containing the name of the subclass variable in data. Essentially the same input to subclass in bal.tab.data.frame().

match.strata

A vector containing matching stratum membership for each unit or a string containing the name of the matching stratum variable in data. Essentially the same input to match.strata in bal.tab.data.frame().

estimand

A character vector; whether the desired estimand is the "ATT", "ATC", or "ATE" for each set of weights. Essentially the same input to estimand in bal.tab.data.frame().

s.weights

A vector containing sampling weights for each unit or a string containing the name of the sampling weight variable in data. Essentially the same input to s.weights in bal.tab.data.frame() or bal.tab.time.list().

focal

The name of the focal treatment when multi-category treatments are used. Essentially the same input to focal in bal.tab.data.frame().

call

A call object containing the function call, usually generated by using match.call() inside the function that created x.

Any of these items can also be supplied directly to bal.tab.default, e.g., bal.tab.default(x, formula = treat ~ x1 + x2). If supplied, it will override the object with the same role in x. In addition, any arguments to bal.tab.formula(), bal.tab.data.frame(), and bal.tab.time.list() are allowed and perform the same function.

At least some inputs containing information to create the treatment and covariates are required (e.g., formula and data or covs and treat). All other arguments are optional and have the same defaults as those in bal.tab.data.frame() or bal.tab.time.list(). If treat.list, covs.list, or formula.list are supplied in x or as an argument to bal.tab.default(), the function will proceed considering a longitudinal treatment. Otherwise, it will proceed considering a point treatment.

bal.tab.default(), like other bal.tab methods, is just a shortcut to supply arguments to bal.tab.data.frame() or bal.tab.time.list(). Therefore, any matters regarding argument priority or function are described in the documentation for these methods.

Value

For point treatments, if clusters and imputations are not specified, an object of class "bal.tab" containing balance summaries for the specified treatment and covariates. See bal.tab() for details.

If clusters are specified, an object of class "bal.tab.cluster" containing balance summaries within each cluster and a summary of balance across clusters. See bal.tab.cluster for details.

If imputations are specified, an object of class "bal.tab.imp" containing balance summaries for each imputation and a summary of balance across imputations, just as with clusters. See bal.tab.imp for details.

If multi-category treatments are used, an object of class "bal.tab.multi" containing balance summaries for each pairwise treatment comparison and a summary of balance across pairwise comparisons. See bal.tab.multi for details.

If longitudinal treatments are used, an object of class "bal.tab.msm" containing balance summaries at each time point. Each balance summary is its own bal.tab object. See bal.tab.msm for more details.

Author

Noah Greifer

See also

bal.tab.data.frame() and bal.tab.time.list() for additional arguments to be supplied. bal.tab() for output and details of calculations. bal.tab.cluster for more information on clustered data. bal.tab.imp for more information on multiply imputed data. bal.tab.multi for more information on multi-category treatments.

Examples

data("lalonde", package = "cobalt")
covs <- subset(lalonde,  select = -c(treat, re78))

##Writing a function the produces output for direct
##use in bal.tab.default

ate.weights <- function(treat, covs) {
    data <- data.frame(treat, covs)
    formula <- formula(data)
    ps <- glm(formula, data = data, 
              family = "binomial")$fitted.values
    weights <- treat/ps + (1-treat)/(1-ps)
    call <- match.call()
    out <- list(treat = treat,
                covs = covs,
                distance = ps,
                weights = weights,
                estimand = "ATE",
                call = call)
    return(out)
}

out <- ate.weights(lalonde$treat, covs)

bal.tab(out, un = TRUE)
#> Call
#>  ate.weights(treat = lalonde$treat, covs = covs)
#> 
#> Balance Measures
#>                Type Diff.Un
#> age         Contin. -0.2419
#> educ        Contin.  0.0448
#> race_black   Binary  0.6404
#> race_hispan  Binary -0.0827
#> race_white   Binary -0.5577
#> married      Binary -0.3236
#> nodegree     Binary  0.1114
#> re74        Contin. -0.5958
#> re75        Contin. -0.2870
#> 
#> Sample sizes
#>     Control Treated
#> All     429     185