Glance at an inverse probability weighted result

glance() describes an ipw() result rather than its estimates: one row naming the estimand and counting the observations and the residual degrees of freedom of the system the standard errors came from. A fit reporting several effect measures, or several contrasts of a categorical exposure, still returns exactly one row.

Under M-estimation that system is the stacked estimating equations, which hold the propensity score model, the outcome model, and the effect measures at once. Its residual degrees of freedom are the observations it was solved on less the parameters it solves for, so a multinomial propensity score model leaves fewer of them than a binary one does on the same data. Linearization stacks nothing and records no parameter count, so the observations are the outcome model's and there is no count to subtract from them.

The columns and their types are the same on every route ipw() takes, so the rows of several results stack into one table.

Usage

# S3 method for class 'ipw'
glance(x, ...)

Arguments

x

An ipw object, as returned by ipw().

...

These dots are for future extensions and must be empty.

Value

A one-row tibble with the columns:

estimand

The estimand the weights target, such as "ate".

nobs

The number of observations the outcome model was fit on, which is also what the stacked estimating equations are solved on under M-estimation. Reported by stats::nobs().

df.residual

The residual degrees of freedom of the stacked estimating equations, nobs less the number of parameters the system solves for. NA under linearization, which records no parameter count. Reported by stats::df.residual().

See also

ipw() for the estimator, tidy() for its estimates, and augment() for its per-observation columns.

Examples

set.seed(123)
n <- 200
x1 <- rnorm(n)
z <- rbinom(n, 1, plogis(0.5 * x1))
y <- rbinom(n, 1, plogis(-0.5 + 0.8 * z + 0.3 * x1))
dat <- data.frame(x1, z, y)

ps_mod <- glm(z ~ x1, data = dat, family = binomial())
wts <- wt_ate(ps_mod)
#> ℹ Using exposure variable "z" from GLM model
#> ℹ Treating `.exposure` as binary
outcome_mod <- glm(y ~ z, data = dat, family = quasibinomial(), weights = wts)
result <- ipw(ps_mod, outcome_mod)

glance(result)
#> # A tibble: 1 × 3
#>   estimand  nobs df.residual
#>   <chr>    <int>       <int>
#> 1 ate        200         191