Choose d-fSPA denoising parameters by cross-validation

At a fixed K, performs k-fold cross-validation over a grid of denoising parameter pairs (N, Delta) for dfspa(). For each candidate pair and each fold, the full MetaHunt pipeline is fit on the out-of-fold studies and predicts the held-out studies' functions. The pair with the lowest average prediction error is selected.

Usage

select_denoising_params(
  F_hat,
  W,
  K,
  N_grid = NULL,
  Delta_grid = NULL,
  n_folds = 5L,
  grid_weights = NULL,
  dfspa_args = list(),
  weight_model_args = list(),
  seed = NULL
)

Arguments

F_hat

An m-by-G_grid numeric matrix of study-level function evaluations.

W

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

K

Integer number of basis functions (fixed during this search).

N_grid

Optional numeric vector of candidate N values. Defaults to c(0.2, 0.5, 1.0) * log(nrow(F_hat)).

Delta_grid

Optional numeric vector of candidate Delta values. Defaults to c(0.05, 0.10, 0.20, 0.30) times the maximum pairwise \(L^2(\mu)\) distance among studies.

n_folds

Integer number of folds (default 5).

grid_weights

Optional length-G_grid non-negative numeric vector.

dfspa_args

Named list of additional arguments for dfspa() (e.g. list(); do not include N, Delta, or denoise here, they are set by the search).

weight_model_args

Named list of additional arguments for fit_weight_model().

seed

Optional integer seed for reproducible fold assignment.

Value

An object of class metahunt_denoising_search: a list with

grid

A data frame with one row per (N, Delta) pair, columns N, Delta, cv_error, cv_se, n_folds_ok.

best

A list with the (N, Delta) minimising cv_error.

K, n_folds, grid_weights

Inputs echoed back for traceability.

Details

This is the cross-validated tuning of the denoising parameters discussed in Section 3.1 of the paper. Joint tuning over (K, N, Delta) is not supported because it scales poorly; if you also want to choose K, do it first via cv_error_curve() and then call this function at the selected K.

Default candidate grids:

• N_grid = m * c(NA, NA, NA) resolved at runtime to c(0.2, 0.5, 1.0) * log(m).

• Delta_grid = max_pairwise_dist * c(0.05, 0.10, 0.20, 0.30).

Pass your own N_grid / Delta_grid (in original units) to override.

See also

cv_error_curve() for selecting K, dfspa() for the underlying basis-hunting algorithm.

Examples

# \donttest{
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), w2 = rnorm(m))
eta <- as.matrix(W) %*% cbind(c(1, -0.5), c(-0.4, 1), c(0, 0))
pi_true <- exp(eta) / rowSums(exp(eta))
F_hat <- pi_true %*% basis + matrix(rnorm(m * G, sd = 0.05), m, G)

tune <- select_denoising_params(F_hat, W, K = 3, n_folds = 4, seed = 1)
#> Warning: 8 (N, Delta, fold) combinations failed:
#>   (N=2.19101, Delta=0.0479349, fold 1): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
#>   (N=2.19101, Delta=0.0479349, fold 2): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
#>   (N=2.19101, Delta=0.0479349, fold 3): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
#>   (N=2.19101, Delta=0.0479349, fold 4): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
#>   (N=4.38203, Delta=0.0479349, fold 1): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
#>   (N=4.38203, Delta=0.0479349, fold 2): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
#>   (N=4.38203, Delta=0.0479349, fold 3): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
#>   (N=4.38203, Delta=0.0479349, fold 4): Only 0 studies survive denoising but K = 3; consider increasing `Delta`, decreasing `N`, or reducing `K`.
tune$grid
#>            N      Delta   cv_error       cv_se n_folds_ok
#> 1  0.8764053 0.04793491 0.07080549 0.002541663          4
#> 2  2.1910133 0.04793491        NaN          NA          0
#> 3  4.3820266 0.04793491        NaN          NA          0
#> 4  0.8764053 0.09586983 0.07454212 0.003150261          4
#> 5  2.1910133 0.09586983 0.07890784 0.003902666          4
#> 6  4.3820266 0.09586983 0.08239793 0.003161490          4
#> 7  0.8764053 0.19173966 0.08429097 0.003326955          4
#> 8  2.1910133 0.19173966 0.08429097 0.003326955          4
#> 9  4.3820266 0.19173966 0.08525947 0.003824418          4
#> 10 0.8764053 0.28760949 0.09502216 0.006381783          4
#> 11 2.1910133 0.28760949 0.09502216 0.006381783          4
#> 12 4.3820266 0.28760949 0.09502216 0.006381783          4
tune$best
#> $N
#> [1] 0.8764053
#> 
#> $Delta
#> [1] 0.04793491
#> 
#> $cv_error
#> [1] 0.07080549
#> 
# }