bal.tab.default: Balance Statistics for Other Objects

Description

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

Usage

# 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,
  ...
)

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 class-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 class-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 class-bal.tab.msm for more details.

Arguments

x: An object containing information about conditioning. See Details.
stats: character; which statistic(s) should be reported. See stats for allowable options. For binary and multi-category treatments, "mean.diffs" (i.e., mean differences) is the default. For continuous treatments, "correlations" (i.e., treatment-covariate Pearson correlations) is the default. Multiple options are allowed.
int: logical or numeric; whether or not to include 2-way interactions of covariates included in covs and in addl. If numeric, will be passed to poly as well.
poly: numeric; the highest polynomial of each continuous covariate to display. For example, if 2, squares of each continuous covariate will be displayed (in addition to the covariate itself); if 3, squares and cubes of each continuous covariate will be displayed, etc. If 1, the default, only the base covariate will be displayed. If int is numeric, poly will take on the value of int.
distance: an optional 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. For longitudinal treatments, can be a list of allowable arguments, one for each time point.
addl: an optional formula or data frame containing additional covariates for which to present balance or a character vector containing their names. If a formula or variable names are specified, bal.tab() will look in the arguments to the input object, covs, and data, if specified. For longitudinal treatments, can be a list of allowable arguments, one for each time point.
data: an optional data frame containing variables named in other arguments. For some input object types, this is required.
continuous: whether mean differences for continuous variables should be standardized ("std") or raw ("raw"). Default "std". Abbreviations allowed. This option can be set globally using set.cobalt.options().
binary: whether mean differences for binary variables (i.e., difference in proportion) should be standardized ("std") or raw ("raw"). Default "raw". Abbreviations allowed. This option can be set globally using set.cobalt.options().
s.d.denom: character; how the denominator for standardized mean differences should be calculated, if requested. See col_w_smd() for allowable options. If weights are supplied, each set of weights should have a corresponding entry to s.d.denom. Abbreviations allowed. If left blank and weights, subclasses, or matching strata are supplied, bal.tab() will figure out which one is best based on the estimand, if given (for ATT, "treated"; for ATC, "control"; otherwise "pooled") and other clues if not.
thresholds: a named vector of balance thresholds, where the name corresponds to the statistic (i.e., in stats) that the threshold applies to. For example, to request thresholds on mean differences and variance ratios, one can set thresholds = c(m = .05, v = 2). Requesting a threshold automatically requests the display of that statistic. When specified, extra columns are inserted into the Balance table describing whether the requested balance statistics exceeded the threshold or not. Summary tables tallying the number of variables that exceeded and were within the threshold and displaying the variables with the greatest imbalance on that balance measure are added to the output.
weights: a vector, list, or data.frame containing weights for each unit, or a string containing the names of the weights variables in data, or an object with a get.w() method or a list thereof. The weights can be, e.g., inverse probability weights or matching weights resulting from a matching algorithm.
cluster: either a vector containing cluster membership for each unit or a string containing the name of the cluster membership variable in data or the input object. See class-bal.tab.cluster for details.
imp: either a vector containing imputation indices for each unit or a string containing the name of the imputation index variable in data or the input object. See class-bal.tab.imp for details. Not necessary if data is a mids object.
pairwise: whether balance should be computed for pairs of treatments or for each treatment against all groups combined. See bal.tab.multi() for details. This can also be used with a binary treatment to assess balance with respect to the full sample.
s.weights: Optional; either a vector containing sampling weights for each unit or a string containing the name of the sampling weight variable in data. These function like regular weights except that both the adjusted and unadjusted samples will be weighted according to these weights if weights are used.
abs: logical; whether displayed balance statistics should be in absolute value or not.
subset: a logical or numeric vector denoting whether each observation should be included or which observations should be included. If logical, it should have length equal to the number of units. NAs will be treated as FALSE. This can be used as an alternative to cluster to examine balance on subsets of the data.
quick: logical; if TRUE, will not compute any values that will not be displayed. Set to FALSE if computed values not displayed will be used later.
...: 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.

Examples

Run this code

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)