R: Compute diagnostics assessing covariates balance.

dx.wts {twang}

R Documentation

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