What a set of weights records about the exposure

Weights carry two records describing the exposure they were built for. exposure_type() returns the type of exposure the weight function was given, and density_meta() returns the record left by weights whose value is a ratio of densities.

Both describe the exposure rather than the units, so neither holds a length of its own: both survive subsetting, arithmetic, and anything else that changes the number of weights. Combining two sets of weights that record either of them differently drops the record they disagree on, with a warning of class propensity_metadata_conflict_warning. A result that no longer records an exposure type is not described as continuous, so it drops any density record along with it.

Usage

exposure_type(wt)

density_meta(wt)

# S3 method for class 'propensity_density_meta'
format(x, ...)

# S3 method for class 'propensity_density_meta'
print(x, ...)

Arguments

wt

A psw or causal_wts object.

x

A density record, as returned by density_meta().

...

Not used.

Value

• exposure_type(): a single string, or NULL.

• density_meta(): a list of class propensity_density_meta with the elements density, numerator, sigma, sigma_value, and numerator_model, or NULL.

• print() and format(): the record, invisibly, and a character vector of one line per choice the record describes.

Details

exposure_type() returns "binary", "categorical", or "continuous". It returns NULL for weights that were built without an exposure to describe, such as those written with psw() directly.

density_meta() returns a record of what the ratio was built from, and NULL for weights that are not a ratio of densities, which is every set of weights for a binary or categorical exposure:

• density, the specification of the conditional density family, as built by dens_normal() and its relatives.

• numerator, what stabilized the weights: "marginal" for the marginal density of the exposure, "integrated" for the conditional density marginalized over the units, "model" for the conditional density a fitted model supplied to stabilize estimates, "score" for a stabilization_score the caller supplied, and "none" for weights that were not stabilized.

• numerator_model, that fitted model itself, and NULL under every other numerator. The model is kept whole rather than the numerator it evaluates to, since ipw() rebuilds that numerator at every value of its parameter vector, which takes the model's design and its coefficients. Two sets of weights stabilized on models that read different terms, or that were fit to different values of the same terms, record different numerators and are combined the way any other disagreeing records are.

• sigma, where the residual spread of the conditional density came from: "pooled" for the pooled residual root mean square, "mle" for a scale estimated under the family that reads it, which dens_t() and dens_laplace() take with sigma_method = "mle", and "supplied" for a .sigma the caller gave. Weights built from a dose model trimmed with ps_trim() record where the trim's spread came from.

• sigma_value, the single spread the weights were read at when it was not estimated by the weight function itself: one the caller supplied, or the spread a dose model trimmed with ps_trim() holds in its record, which is recorded whatever its source. It is NULL for a spread estimated from the residuals, by either estimator, and for one supplied per observation. A spread that is one number is a constant the weights can be rebuilt from, which is what ipw() needs of it; a spread that changes with the observation is not, so the record holds where it came from and nothing more.

Weights that carry a density record print it under their values, as the lines format() renders: the density family, the numerator, and the spread, followed by the formula of the model supplied to stabilize when a model estimated the numerator. Weights that carry none, which is every set of weights for a binary or categorical exposure, print exactly as they always have.

See also

psw() for the weight vector class and the rest of what it records, and wt_ate() for the functions that write these records.

Examples

set.seed(1)
ps <- runif(20, 0.2, 0.8)
trt <- rbinom(20, 1, ps)

# A binary exposure is weighted by propensity scores rather than by a
# density, so there is no density to record.
exposure_type(wt_ate(ps, trt))
#> ℹ Treating `.exposure` as binary
#> [1] "binary"
density_meta(wt_ate(ps, trt))
#> ℹ Treating `.exposure` as binary
#> NULL

dose <- rnorm(20)
mu <- 0.3 * ps
w <- wt_ate(mu, dose, exposure_type = "continuous")

exposure_type(w)
#> [1] "continuous"
density_meta(w)
#> density:   normal
#> numerator: marginal
#> sigma:     pooled