Balance Statistics for sbw Objects — bal.tab.sbwcau • cobalt

Generates balance statistics for sbwcau objects from sbw.

Usage

# S3 method for sbwcau
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 sbwcau object; the output of a call to sbw::sbw().
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. Abbreviations allowed. If not specified, bal.tab() will figure out which one is best based on the par component of the sbwcau object: if "att", "treated"; if "atc", "control"; otherwise "pooled".
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.
...: 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

If clusters are not specified, an object of class "bal.tab" containing balance summaries for the sbwcau 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.

Details

bal.tab.sbwcau() generates a list of balance summaries for the sbwcau object given, and functions similarly to sbw::summarize().

Examples

library(sbw); data("lalonde", package = "cobalt")
#> Loading required package: Matrix
#> Loading required package: quadprog
#> Loading required package: slam

## Stable balancing weights for the ATT
sbw.out <- sbw(splitfactor(lalonde, drop.first = "if2"),
               ind = "treat",
               bal = list(bal_cov = c("age", "educ", "race_black", 
                                      "race_hispan", "race_white", 
                                      "married", "nodegree", 
                                      "re74", "re75"),
                          bal_alg = FALSE, 
                          bal_tol = .001),
               par = list(par_est = "att"))
#>   quadprog optimizer is opening... 
#>   Finding the optimal weights... 
#>   Optimal weights found. 

bal.tab(sbw.out, un = TRUE, poly = 2)
#> Balance Measures
#>                Type Diff.Un Diff.Adj
#> age         Contin. -0.3094   0.0015
#> educ        Contin.  0.0550   0.0014
#> race_black   Binary  0.6404   0.0004
#> race_hispan  Binary -0.0827   0.0001
#> race_white   Binary -0.5577  -0.0005
#> married      Binary -0.3236  -0.0005
#> nodegree     Binary  0.1114   0.0005
#> re74        Contin. -0.7211  -0.0014
#> re75        Contin. -0.2903   0.0010
#> age²        Contin. -0.4276  -0.1698
#> educ²       Contin. -0.0468  -0.0674
#> re74²       Contin. -0.4331   0.0510
#> re75²       Contin. -0.0757   0.0483
#> 
#> Effective sample sizes
#>            Control Treated
#> Unadjusted  429.       185
#> Adjusted    108.99     185

Balance Statistics for `sbw` Objects

Usage

Arguments

Value

Details

See also

Examples