R: Calculate weighted balance statistics

bal.stat {twang}

R Documentation

Calculate weighted balance statistics

Description

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

Usage

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`	Oobservation 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`	logical. 'TRUE' if used for multinomial propensity scores.
`fillNAs`	logical. 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.

References

Dan McCaffrey, G. Ridgeway, Andrew Morral (2004). "Propensity Score Estimation with Boosted Regression for Evaluating Adolescent Substance Abuse Treatment", *Psychological Methods* 9(4):403-425.