Estimate Density Ratios (backwards-compatible wrapper)

Wraps fit_density_ratio() + predict_density_ratio() on the same data. Preserved for backwards compatibility with kernR 0.0.0.901x callers; new code should prefer the explicit fit/predict pair so train/test splits are honoured.

Usage

estimate_density_ratio(
  x,
  z,
  method = c("logistic", "ranger", "xgboost", "proxymix"),
  n_noise = 1L,
  proxymix_components = 2L,
  seed = NULL
)

Arguments

x

Numeric vector or matrix. Treatment variable (training).

z

Numeric matrix or data.frame. Confounders (training).

method

Character. Backend: "logistic" (default), "ranger", "xgboost", or "proxymix".

n_noise

Integer. Noise samples per real sample for classifier backends. Default 1L.

proxymix_components

Integer. Mixture components per density when method = "proxymix". Default 2L.

seed

Integer or NULL. Random seed.

Value

A list of class density_ratio_fit_estimate with components weights, ratios, ess, method, n, and fit (the underlying density_ratio_fit for callers that want diagnostics).

Details

The return shape (weights, ratios, ess, method, n) is unchanged from previous versions. Internally, ratios are now computed in log-space (which fixes pathological tail behaviour that the classifier-based 0.0.0.9012 implementation occasionally showed under extreme imbalance).

See also

fit_density_ratio() for the fit/predict surface.

Other density ratio and propensity: assess_overlap(), effective_sample_size(), estimate_propensity(), fit_density_ratio(), plot_weights(), predict_density_ratio()

Examples

set.seed(42)
n <- 200
z <- matrix(rnorm(n * 2), n, 2)
x <- z[, 1] + rnorm(n)
dr <- estimate_density_ratio(x, z)
dr$ess
#> [1] 198.4936