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Build the F_hat matrix from a list of fitted study-level models

Source: R/onramp.R
f_hat_from_models.Rd

Many users arrive at MetaHunt with one fitted model per study (e.g. a ranger::ranger() random forest or a grf::causal_forest()) and a chosen evaluation grid. f_hat_from_models() evaluates each model on the shared grid and stacks the predictions into the m-by-G_grid matrix the rest of the package expects.

Usage

f_hat_from_models(models, grid, predict_fn = NULL)

Arguments

models

A non-empty list of fitted model objects, one per study.

grid

A data frame (or matrix) of grid points at which to evaluate each model. Same columns as the data each model was fitted on.

predict_fn

Optional function(model, grid) returning a numeric vector; defaults to the class-aware dispatcher above.

Value

An length(models)-by-nrow(grid) numeric matrix; row i is model i evaluated at every row of grid.

Details

By default the function dispatches on the model class:

  • ranger objects are evaluated as predict(model, data = grid)$predictions.

  • Objects inheriting from causal_forest or grf are evaluated as predict(model, newdata = grid)$predictions.

  • All other classes fall through to as.numeric(predict(model, newdata = grid)), which works for lm, glm, randomForest, and most other R model objects.

Override the dispatch with predict_fn = function(model, grid) ... if your models need bespoke handling. The function must return a length-G_grid numeric vector for each model.

All rows of the returned matrix must have the same length (G_grid) and contain no NA values; the function errors otherwise.

See also

build_grid() to construct grid from a reference dataset.

Examples

# Toy example: each "centre" fits a polynomial regression
set.seed(1)
make_centre_data <- function(slope) {
  x <- runif(60)
  data.frame(x = x, y = slope * x + rnorm(60, sd = 0.1))
}
models <- lapply(c(-1, 0, 1, 0.5, -0.5), function(s)
  stats::lm(y ~ poly(x, 2), data = make_centre_data(s)))

grid  <- data.frame(x = seq(0, 1, length.out = 30))
F_hat <- f_hat_from_models(models, grid)
dim(F_hat)   # 5 x 30
#> [1]  5 30