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