Fit the full MetaHunt pipeline

End-to-end convenience wrapper that runs the three training-time steps of the MetaHunt pipeline in sequence: (1) dfspa() for basis hunting, (2) project_to_simplex() for per-study weight recovery, and (3) fit_weight_model() for modelling the weight-to-covariate map. The result supports predict.metahunt() for generating target-function predictions on new study-level covariates.

Usage

metahunt(
  F_hat,
  W,
  K,
  grid_weights = NULL,
  dfspa_args = list(),
  weight_model_args = list()
)

Arguments

F_hat

An m-by-G_grid numeric matrix of study-level function evaluations on a shared grid; row i is \(\hat f^{(i)}\).

W

An m-by-p matrix or data frame of study-level covariates.

K

Integer number of basis functions.

grid_weights

Optional length-G_grid non-negative numeric vector defining the \(L^2(\mu)\) inner product; defaults to uniform.

dfspa_args

Named list of extra arguments for dfspa().

weight_model_args

Named list of extra arguments for fit_weight_model().

Value

An object of class "metahunt": a list with the dfspa_fit, weight_model, training pi_hat, K, and a stored copy of grid_weights.

Details

For uncertainty quantification, pair a "metahunt" fit with conformal_from_fit() (requires a separate calibration set). The high-level split_conformal() and cross_conformal() functions perform their own fitting and do not consume a pre-fit "metahunt" object.

See also

predict.metahunt(), split_conformal(), cross_conformal(), conformal_from_fit(), reconstruction_error_curve(), cv_error_curve().

Examples

set.seed(1)
G <- 40; m <- 80
x <- seq(0, 1, length.out = G)
basis <- rbind(sin(pi * x), cos(pi * x), x)
W <- data.frame(w1 = rnorm(m), w2 = rnorm(m))
eta <- as.matrix(W) %*% cbind(c(1, -0.8), c(-0.5, 1.2), c(0, 0))
pi_true <- exp(eta) / rowSums(exp(eta))
F_hat <- pi_true %*% basis + matrix(rnorm(m * G, sd = 0.05), m, G)

fit <- metahunt(F_hat, W, K = 3)
fit
#> MetaHunt fit
#>   m (studies):    80 
#>   G (grid size):  40 
#>   K (bases):      3 
#>   weight method:  dirichlet 
#>   predictors:     w1, w2 
f_pred <- predict(fit, newdata = W[1:3, ])
dim(f_pred)                                  # 3 x G: predicted functions
#> [1]  3 40
predict(fit, newdata = W[1:3, ], wrapper = mean)  # scalar summaries
#> [1] 0.39676714 0.38963182 0.08180082