Summarise a MetaHunt fit

Produces a compact summary of a "metahunt" object, including study/grid sizes, the weight-model method, per-basis summary statistics for the training simplex weights pi_hat, and denoising bookkeeping from the underlying dfspa() fit.

Usage

# S3 method for class 'metahunt'
summary(object, ...)

Arguments

object

A "metahunt" object from metahunt().

...

Ignored.

Value

An object of class "summary.metahunt": a list with components

m

Number of studies.

G_grid

Grid size.

K

Number of basis functions.

weight_method

Method used by the weight model.

predictor_names

Character vector of covariate names.

pi_summary

A K-by-5 numeric matrix; each row gives min, mean, median, max, and sd of the corresponding column of object$pi_hat.

n_kept

Number of studies retained after denoising.

n_dropped

Number of studies dropped (m - n_kept).

denoising

List with N and Delta from the dfspa fit (both NA when denoise = FALSE).

Examples

set.seed(1)
G <- 25; m <- 40
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, dfspa_args = list(denoise = FALSE))
summary(fit)
#> MetaHunt fit summary
#>   m (studies):    40 
#>   G (grid size):  25 
#>   K (bases):      3 
#>   weight method:  dirichlet 
#>   predictors:     w1, w2 
#>   studies kept:   40 
#>   studies dropped: 0 
#>   denoising N:    NA 
#>   denoising Delta: NA 
#> 
#> Per-basis pi_hat summary:
#>         min   mean median max     sd
#> basis_1   0 0.3099 0.2376   1 0.2845
#> basis_2   0 0.3117 0.1956   1 0.3204
#> basis_3   0 0.3784 0.3983   1 0.2214