dx.wts: Compute diagnostics assessing covariates balance.

In twang: Toolkit for Weighting and Analysis of Nonequivalent Groups

Compute diagnostics assessing covariates balance.

Description

dx.wts takes a ps object or a set of propensity scores and computes diagnostics assessing covariates balance.

Usage

dx.wts(
  x,
  data,
  estimand,
  vars = NULL,
  treat.var,
  x.as.weights = TRUE,
  sampw = NULL,
  perm.test.iters = 0
)

Arguments

x	A data frame, matrix, or vector of propensity score weights or a ps object. x can also be a data frame, matrix, or vector of propensity scores if x.as.weights=FALSE.
data	A data frame.
estimand	The estimand of interest: either "ATT" or "ATE".
vars	A vector of character strings naming variables in data on which to assess balance.
treat.var	A character string indicating which variable in data contains the 0/1 treatment group indicator.
x.as.weights	TRUE or FALSE indicating whether x specifies propensity score weights or propensity scores. Ignored if x is a ps object. Default: TRUE.
sampw	Optional sampling weights. If x is a ps object, then the sampling weights should have been passed to ps and not specified here. dx.wts will issue a warning if x is a ps object and sampw is also specified.
perm.test.iters	A non-negative integer giving the number of iterations of the permutation test for the KS statistic. If perm.test.iters=0, then the function returns an analytic approximation to the p-value. This argument is ignored is x is a ps object. Setting perm.test.iters=200 will yield precision to within 3% if the true p-value is 0.05. Use perm.test.iters=500 to be within 2%.

Details

Creates a balance table that compares unweighted and weighted means and standard deviations, computes effect sizes, and KS statistics to assess the ability of the propensity scores to balance the treatment and control groups.

Value

Returns a list containing

• treat The vector of 0/1 treatment assignment indicators.

See Also

ps

twang documentation built on Sept. 11, 2024, 8:47 p.m.