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Balance Statistics for sbw Objects

bal.tab.sbw.Rd

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

see bal.tab() for details.

See below for a special note on the s.d.denom argument.

The following argument has a special note when used with sbwcau objects:

s.d.denom

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".

Details

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

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 bal.tab.cluster for details.

Author

Noah Greifer

See also

bal.tab() for details of calculations.

Examples

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

## 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