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What a set of weights records about the exposure

Source: R/psw.R
exposure_type.Rd

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, and sigma_value, or NULL.

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

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, "score" for a stabilization_score the caller supplied, and "none" for weights that were not stabilized.

  • sigma, where the residual spread of the conditional density came from: "pooled" for the pooled residual standard deviation, and "supplied" for a .sigma the caller gave.

  • sigma_value, the single spread the caller supplied, and NULL for a pooled spread 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.

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