numerator_model() returns the fitted model supplied to stabilize that
estimated the numerator of a set of weights, and NULL for weights whose
numerator was not estimated by one.
Details
A model supplied to wt_ate()'s stabilize argument estimates the
numerator of the weights, and what it estimates follows the exposure. A
binomial() fit of a binary exposure reports the probability of the level
each unit took; an nnet::multinom() fit of a categorical exposure reports
one probability per level, and the numerator is the column named for the
level each unit took; a model of a dose reports the conditional mean the
density family is read at, and the numerator is that density.
The model itself is recorded rather than the numerator it evaluates to,
because ipw() rebuilds that numerator at every value of its parameter
vector, which takes the model's design and its coefficients.
Where the record is kept differs by exposure type, and this accessor is the
one place to read it either way. Weights for a continuous exposure are a
ratio of densities and keep the model inside the density record
density_meta() returns; weights for a binary or categorical exposure are a
ratio of probabilities, leave no density record, and keep the model on its
own. Both come back here.
The record describes the numerator rather than the units, so it holds no
length of its own and survives subsetting, arithmetic, and anything else that
changes the number of weights. Two sets of weights record the same numerator
when their models write the same formula and were fit to the same
coefficients; combining two that record different ones drops the record, with
a warning of class propensity_metadata_conflict_warning. Weights stabilized
without a model were divided by a numerator no model estimated, which is a
different numerator again: combining those with weights stabilized on a model
drops the model the same way, and the result is stabilized on nothing it can
name.
A stabilization_score is a numerator the caller computed rather than one a
model estimated, so weights stabilized on a score record no model, and
stabilization_score() returns NULL for weights stabilized on a model.
See also
wt_ate() for the stabilize argument that writes this record,
stabilization_score() for the numerator a caller supplies as a vector,
and density_meta() for the rest of what a continuous exposure's weights
record.
Examples
set.seed(1)
x <- rnorm(50)
v <- rbinom(50, 1, 0.5)
z <- rbinom(50, 1, plogis(0.4 * x - 0.7 * v))
ps_mod <- glm(z ~ x + v, family = binomial())
num_mod <- glm(z ~ v, family = binomial())
w <- wt_ate(ps_mod, stabilize = num_mod)
#> ℹ Using exposure variable "z" from the propensity score model
#> ℹ Treating `.exposure` as binary
numerator_model(w)
#>
#> Call: glm(formula = z ~ v, family = binomial())
#>
#> Coefficients:
#> (Intercept) v
#> -0.3365 -0.1335
#>
#> Degrees of Freedom: 49 Total (i.e. Null); 48 Residual
#> Null Deviance: 67.3
#> Residual Deviance: 67.25 AIC: 71.25
# Weights stabilized on the marginal probability record no model.
numerator_model(wt_ate(ps_mod, stabilize = TRUE))
#> ℹ Using exposure variable "z" from the propensity score model
#> ℹ Treating `.exposure` as binary
#> NULL