dx.wts
takes a ps
object or a set of propensity scores and
computes diagnostics assessing covariates balance.
dx.wts(
x,
data,
estimand,
vars = NULL,
treat.var,
x.as.weights = TRUE,
sampw = NULL,
perm.test.iters = 0
)
Returns a list containing
treat
The vector of 0/1 treatment assignment indicators.
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
.
A data frame.
The estimand of interest: either "ATT" or "ATE".
A vector of character strings naming variables in data
on
which to assess balance.
A character string indicating which variable in data
contains the 0/1 treatment group indicator.
TRUE
or FALSE
indicating whether x
specifies propensity score weights or propensity scores.
Ignored if x
is a ps object. Default: TRUE
.
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.
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%.
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.
ps