Balance Statistics for twang Objects — bal.tab.ps • cobalt

Generates balance statistics for ps, mnps, and iptw objects from twang and for ps.cont objects from twangContinuous.

Usage

# S3 method for class 'ps'
bal.tab(
  x,
  stop.method,
  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: a ps, mnps, iptw, or ps.cont object; the output of a call to twang::ps(), twang::mnps(), twang::iptw() or twangContinuous::ps.cont().
stop.method: a string containing the names of the stopping methods used in the original call to ps(), mnps(), or iptw(). Examples include "es.max" or "ks.mean" for ps and mnps objects. bal.tab() will assess balance for the weights created by those stopping methods. The names can be abbreviated as long as the abbreviations are specific enough. If no stopping methods are provided, bal.tab() will default to displaying balance for all available stopping methods. Ignored for ps.cont objects.
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. The propensity scores generated by ps() and iptw() (but not mnps() or ps.cont()) are automatically included and named "prop.score.{stop.method}".
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. Abbreviations allowed. If not specified, for ps objects, bal.tab() will use "treated" if the estimand of the call to ps() is the ATT and "pooled" if the estimand is the ATE; for mnps objects, bal.tab() will use "treated" if treatATT was specified in the original call to mnps and "pooled" otherwise. Use "all" to get the same values computed by bal.table() in twang.
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. If sampw was supplied in the call to ps(), mnps(), iptw(), or ps.cont(), they will automatically be supplied to s.weights and do not need be specified again (though there is no harm if they are).
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.
...: for some input types, other arguments that are required or allowed. Otherwise, further arguments to control display of output. See display options for details.

Value

For binary or continuous point treatments, if clusters are not specified, an object of class "bal.tab" containing balance summaries for the ps object. 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 mnps() is used with multi-category treatments, 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.

Details

bal.tab.ps() generates a list of balance summaries for the input object given, and functions similarly to twang::bal.table(). The variances used in the denominator of the standardized mean differences computed in twang::bal.table() are weighted and computed using survey::svyvar() and are unweighted here (except when s.weights are specified, in which case col_w_sd() is used). twang also uses "all" as the default s.d.denom when the estimand is the ATE; the default here is "pooled". For these reasons, results may differ slightly between the two packages.

Examples

library(twang)
#> To reproduce results from prior versions of the twang package, please see the version="legacy" option described in the documentation.
data("lalonde", package = "cobalt")

## Using ps() for generalized boosted modeling
ps.out <- ps(treat ~ age + educ + married + race +
                 nodegree + re74 + re75, data = lalonde, 
             stop.method = c("ks.mean", "es.mean"), 
             estimand = "ATT", verbose = FALSE)

bal.tab(ps.out, stop.method = "ks.mean", un = TRUE, 
        stats = c("m", "ks"),
        thresholds = c(m = .1))
#> Balance Measures
#>                 Type Diff.Un  KS.Un Diff.Adj        M.Threshold KS.Adj
#> prop.score  Distance  2.8072 0.8294   0.5712                    0.2164
#> age          Contin. -0.3094 0.1577   0.0538     Balanced, <0.1 0.0980
#> educ         Contin.  0.0550 0.1114  -0.0810     Balanced, <0.1 0.0678
#> married       Binary -0.3236 0.3236   0.0029     Balanced, <0.1 0.0029
#> race_black    Binary  0.6404 0.6404   0.0176     Balanced, <0.1 0.0176
#> race_hispan   Binary -0.0827 0.0827   0.0014     Balanced, <0.1 0.0014
#> race_white    Binary -0.5577 0.5577  -0.0191     Balanced, <0.1 0.0191
#> nodegree      Binary  0.1114 0.1114   0.0637     Balanced, <0.1 0.0637
#> re74         Contin. -0.7211 0.4470   0.1060 Not Balanced, >0.1 0.0591
#> re75         Contin. -0.2903 0.2876   0.1217 Not Balanced, >0.1 0.0941
#> 
#> Balance tally for mean differences
#>                    count
#> Balanced, <0.1         7
#> Not Balanced, >0.1     2
#> 
#> Variable with the greatest mean difference
#>  Variable Diff.Adj        M.Threshold
#>      re75   0.1217 Not Balanced, >0.1
#> 
#> Effective sample sizes
#>            Control Treated
#> Unadjusted   429.      185
#> Adjusted      25.5     185

Balance Statistics for `twang` Objects

Usage

Arguments

Value

Details

See also

Examples