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Split conformal intervals from a pre-fit MetaHunt pipeline

Source: R/conformal.R
conformal_from_fit.Rd

Lower-level entry point that builds split conformal intervals for new target covariates using an already-fitted d-fSPA basis decomposition, an already-fitted weight model, and a user-supplied calibration set. Use this when you have independently tuned K or want to reuse a pipeline fit; otherwise the high-level split_conformal() is usually more convenient.

Usage

conformal_from_fit(
  dfspa_fit,
  weight_model,
  F_cal,
  W_cal,
  W_new,
  alpha = 0.05,
  wrapper = NULL,
  grid_weights = NULL
)

Arguments

dfspa_fit

A "dfspa" object from dfspa().

weight_model

A "metahunt_weight_model" object from fit_weight_model(). Must satisfy weight_model$K == nrow(dfspa_fit$bases).

F_cal

An n_cal-by-G_grid numeric matrix of observed study-level function evaluations for the calibration set. Calibration studies must be independent of both dfspa_fit and weight_model for valid coverage.

W_cal

A matrix or data frame of study-level covariates for the calibration set, with the same columns used to fit weight_model.

W_new

A matrix or data frame of study-level covariates for new target studies.

alpha

Miscoverage level; default 0.05.

wrapper

Optional reduction function (see apply_wrapper()). If NULL, intervals are pointwise at every grid point.

grid_weights

Optional length-G_grid non-negative numeric vector used for the default wrapper and for weighted inner products.

Value

An object of class "metahunt_conformal"; see split_conformal() for a description of its fields.

See also

split_conformal() for the high-level version that splits and fits internally, cross_conformal() for the K-fold variant.

Examples

set.seed(1)
G <- 30; m <- 80
x <- seq(0, 1, length.out = G)
basis <- rbind(sin(pi * x), cos(pi * x), x)
W <- data.frame(w1 = rnorm(m))
eta <- cbind(0.8 * W$w1, -0.4 * W$w1, rep(0, m))
pi_true <- exp(eta) / rowSums(exp(eta))
F_hat <- pi_true %*% basis + matrix(rnorm(m * G, sd = 0.05), m, G)

# user-controlled split and fit
tr  <- 1:50; cal <- 51:70; new <- 71:80
fit <- dfspa(F_hat[tr, ], K = 3)
pih <- project_to_simplex(F_hat[tr, ], fit$bases)
wm  <- fit_weight_model(pih, W[tr, , drop = FALSE])

res <- conformal_from_fit(
  fit, wm,
  F_cal = F_hat[cal, ], W_cal = W[cal, , drop = FALSE],
  W_new = W[new, , drop = FALSE],
  wrapper = mean
)
res
#> MetaHunt conformal prediction
#>   method:        split 
#>   alpha:         0.05 
#>   n calibration: 20 
#>   mode:          scalar (via wrapper)
#>   n targets:     10 
#>   quantile:      0.1492