desc.wts
assesses the quality of a set of weights on balancing a treatment
and control group.
desc.wts(data,
w,
sampw = sampw,
vars = NULL,
treat.var,
tp,
na.action = "level",
perm.test.iters=0,
verbose=TRUE,
alerts.stack,
estimand, multinom = FALSE, fillNAs = FALSE)
See the description of the desc
component of the ps
object that
ps
returns
a data frame containing the dataset
a vector of weights equal to nrow(data)
sampling weights, if provided
a vector of variable names corresponding to data
the name of the treatment variable
a title for the method ``type" used to create the weights, used to label the results
a string indicating the method for handling missing data
an 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%
if TRUE, lots of information will be printed to monitor the the progress of the fitting
an object for collecting warnings issued during the analyses
the estimand of interest: either "ATT" or "ATE"
Indicator that weights are from a propsensity score analysis with 3 or more treatment groups.
If TRUE
fills NAs with zeros.
desc.wts
calls bal.stat
to assess covariate balance.
If perm.test.iters>0
it will call bal.stat
multiple
times to compute Monte Carlo p-values for the KS statistics and the maximum KS
statistic. It assembles the results into a list object, which usually becomes
the desc
component of ps objects that ps
returns.
ps