Calculate weighted balance statistics

Description

bal.stat compares the treatment and control subjects by means, standard deviations, effect size, and KS statistics

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
bal.stat(data, 
         vars = NULL, 
         treat.var, 
         w.all,
         sampw, 
         get.means = TRUE,
         get.ks = TRUE, 
         na.action = "level",
         estimand,
         multinom, fillNAs = FALSE)

Arguments

data

a data frame containing the data

vars

a vector of character strings with the names of the variables on which the function will assess the balance

treat.var

the name of the treatment variable

w.all

observation weights (e.g. propensity score weights, sampling weights, or both)

sampw

sampling weights. These are passed in addition to w.all because the "unweighted" results shoud be adjusted for sample weights (though not propensity score weights).

get.means

logical. If TRUE then bal.stat will compute means and variances

get.ks

logical. If TRUE then bal.stat will compute KS statistics

na.action

a character string indicating how bal.stat should handle missing values. Current options are "level", "exclude", or "lowest"

estimand

either "ATT" or "ATE"

multinom

TRUE if used for multinomial propensity scores.

fillNAs

If TRUE, fills in zeros for missing values.

Details

bal.stat calls auxiliary functions for each variable and assembles the results in a table

Value

get.means and get.ks manipulate the inclusion of certain columns in the returned result.

See Also

The example for ps contains an example of the use of bal.table

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.