PSW (version 1.1-3)

psw.balance: Balance checking using standardized mean difference

Description

psw.balance is used to compute the standardized mean difference (in percentage) for balance diagnosis.

Usage

psw.balance(data, form.ps, weight, V.name = NULL, K = 4)

Arguments

data

data frame to be used.

form.ps

propensity score model.

weight

weighting method to be used. Available methods are "ATE", "ATT", "ATC", "MW", "OVERLAP", and "TRAPEZOIDAL".

V.name

a vector of covariates on which standardized mean difference is computed. If V.name = NULL, the covariates in propensity score model are used.

K

value of \(K\) in \(\omega(e_i) = min(1, K min(e_i, 1-e_i)) \) for "TRAPEZOIDAL" weight.

Value

A list of weighting method, fitted propensity score model, estimated propenstity scores, estimated propensity score weights, standardized mean difference before and after weighting adjustment.

weight

weighting method.

ps.model

object returned by fitting the propensity score model using glm with "binomial" family.

ps.hat

estimated propensity score.

W

estimated propensity score weight.

std.diff.before

A data frame of weighed mean, variance, and standardized mean difference for covariates in V.name by treatment groups before weighting. V.name is the row name and "treated.mean", "treated.var", "control.mean", "control.var", "std.diff.pct" are column names.

std.diff.after

A data frame of weighed mean, variance, and standardized mean difference for covariates in V.name by treatment groups after weighting.

See Also

psw, psw.spec.test

Examples

# NOT RUN {
# Load the test data set
data(test_data);
# Propensity score model
form.ps <- "Z ~ X1 + X2 + X3 + X4";
# A vector of covariates
V.name <- c( "X1", "X2", "X3", "X4" );
tmp <- psw.balance( data = test_data, weight = "MW", form.ps = form.ps,
V.name = V.name );

# }