R: Add sampling weights to a 'matchit' object

add_s.weights {MatchIt} | R Documentation

## 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)

### 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.

### Value

a `matchit` object with an `s.weights` component containing the supplied sampling weights. The `nn` component containing the sample sizes before and after matching will be adjusted to incorporate the sampling weights. If `s.weights = NULL`, the original `matchit` object is returned.

### Author(s)

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)

[Package MatchIt version 4.3.3 Index]