MatchIt (version 4.5.5)

add_s.weights: Add sampling weights to a matchit object

Description

Adds sampling weights to a matchit object so that they are incorporated into balance assessment and creation of the weights. This would typically only be used when an argument to s.weights was not supplied to matchit() (i.e., because they were not to be included in the estimation of the propensity score) but sampling weights are required for generalizing an effect to the correct population. Without adding sampling weights to the matchit object, balance assessment tools (i.e., summary.matchit() and plot.matchit()) will not calculate balance statistics correctly, and the weights produced by match.data() and get_matches() will not incorporate the sampling weights.

Usage

add_s.weights(m, s.weights = NULL, data = NULL)

Value

a matchit object with an s.weights component containing the supplied sampling weights. If s.weights = NULL, the original matchit object is returned.

Arguments

m

a matchit object; the output of a call to matchit(), typically with the s.weights argument unspecified.

s.weights

an numeric vector of sampling weights to be added to the matchit object. Can also be specified as a string containing the name of variable in data to be used or a one-sided formula with the variable on the right-hand side (e.g., ~ SW).

data

a data frame containing the sampling weights if given as a string or formula. If unspecified, add_s.weights() will attempt to find the dataset using the environment of the matchit object.

Author

Noah Greifer

See Also

matchit(); match.data()

Examples

data("lalonde")

# Generate random sampling weights, just
# for this example
sw <- rchisq(nrow(lalonde), 2)

# NN PS match using logistic regression PS that doesn't
# include sampling weights
m.out <- matchit(treat ~ age + educ + race + nodegree +
                   married  + re74 + re75, data = lalonde)

m.out

# Add s.weights to the matchit object
m.out <- add_s.weights(m.out, sw)

m.out #note additional output

# Check balance; note that sample sizes incorporate
# s.weights
summary(m.out, improvement = FALSE)