Reduce predicted functions to scalars via a user-supplied wrapper

Many downstream quantities of interest (average treatment effect, pointwise predictions, or other functionals of \(f^{(0)}\)) are scalar summaries of the predicted function. apply_wrapper() applies any user-supplied reduction to each row of a function matrix, with a default of the weighted mean with respect to grid_weights (which coincides with \(\int f\,d\mu\) when grid_weights represents \(\mu\)).

Usage

apply_wrapper(F_mat, wrapper = NULL, grid_weights = NULL)

Arguments

F_mat

An n-by-G_grid numeric matrix; each row a function on a shared grid (e.g. the output of predict_target()).

wrapper

Either NULL (default, weighted mean) or a function that takes a single numeric vector of length G_grid and returns a scalar. Examples: mean, median, max, function(f) f[17] (point evaluation), function(f) sum(f^2).

grid_weights

Optional length-G_grid non-negative numeric vector. Used only when wrapper = NULL. Defaults to uniform 1 / G_grid.

Value

A length-n numeric vector of scalar summaries.

Examples

F_mat <- matrix(1:12, nrow = 3, byrow = TRUE)  # 3 "functions" on a 4-point grid
apply_wrapper(F_mat)                            # row means (uniform grid weights)
#> [1]  2.5  6.5 10.5
apply_wrapper(F_mat, wrapper = max)             # row maxes
#> [1]  4  8 12
apply_wrapper(F_mat, wrapper = function(f) f[2]) # point evaluation at grid idx 2
#> [1]  2  6 10