svem_significance_test_parallel: SVEM Significance Test with Mixture Support (Parallel...

In SVEMnet: Self-Validated Ensemble Models with Lasso and Relaxed-Elastic Net Regression

SVEM Significance Test with Mixture Support (Parallel Version)

Description

Whole-model significance test using SVEM with support for mixture factor groups, parallelizing the SVEM fits for originals and permutations.

Usage

svem_significance_test_parallel(
  formula,
  data,
  mixture_groups = NULL,
  nPoint = 2000,
  nSVEM = 10,
  nPerm = 150,
  percent = 90,
  nBoot = 100,
  glmnet_alpha = c(1),
  weight_scheme = c("SVEM"),
  objective = c("auto", "wAIC", "wBIC", "wGIC", "wSSE"),
  auto_ratio_cutoff = 1.3,
  gamma = 2,
  relaxed = FALSE,
  verbose = TRUE,
  nCore = parallel::detectCores(),
  seed = NULL,
  ...
)

Arguments

formula	A formula specifying the model to be tested.
data	A data frame containing the variables in the model.
mixture_groups	Optional list describing one or more mixture factor groups. Each element of the list should be a list with components vars (character vector of column names), lower (numeric vector of lower bounds of the same length as vars), upper (numeric vector of upper bounds of the same length), and total (scalar specifying the sum of the mixture variables). All mixture variables must be included in vars, and no variable can appear in more than one mixture group. Defaults to NULL (no mixtures).
nPoint	Number of random points in the factor space (default: 2000).
nSVEM	Number of SVEM fits on the original data (default: 10).
nPerm	Number of SVEM fits on permuted responses for the reference distribution (default: 150).
percent	Percentage of variance to capture in the SVD (default: 90).
nBoot	Number of bootstrap iterations within each SVEM fit (default: 100).
glmnet_alpha	The alpha parameter(s) for glmnet (default: c(1)).
weight_scheme	Weighting scheme for SVEM (default: "SVEM").
objective	Objective used inside SVEMnet() to pick the bootstrap path solution. One of "auto", "wAIC", "wBIC", "wGIC", "wSSE" (default: "auto").
auto_ratio_cutoff	Single cutoff for the automatic rule when objective = "auto" (default 1.3). With r = n_X/p_X, if r >= auto_ratio_cutoff use wAIC; else wBIC. Passed to SVEMnet().
gamma	Penalty weight used only when objective = "wGIC" (default 2). Passed to SVEMnet().
relaxed	Logical; default FALSE. When TRUE, inner SVEMnet() fits use glmnet's relaxed elastic net path and select both lambda and relaxed gamma on each bootstrap. When FALSE, the standard glmnet path is used. This value is passed through to SVEMnet(). Any relaxed provided via ... is ignored with a warning.
verbose	Logical; if TRUE, displays progress messages (default: TRUE).
nCore	Number of CPU cores for parallel processing (default: all available cores).
seed	Optional integer seed for reproducible parallel RNG (default: NULL).
...	Additional arguments passed to SVEMnet() and then to glmnet() (for example: penalty.factor, offset, lower.limits, upper.limits, standardize.response, etc.). The relaxed setting is controlled by the relaxed argument of this function and any relaxed value passed via ... is ignored with a warning.

Details

Identical in logic to svem_significance_test() but runs the expensive SVEM refits in parallel using foreach + doParallel. Random draws (including permutations) use RNGkind("L'Ecuyer-CMRG") for parallel-suitable streams.

Value

A list of class svem_significance_test containing the test results.

See Also

svem_significance_test

Examples

  set.seed(1)

  # Small toy data with a 3-component mixture A, B, C
  n <- 40
  sample_trunc_dirichlet <- function(n, lower, upper, total) {
    k <- length(lower)
    stopifnot(length(upper) == k, total >= sum(lower), total <= sum(upper))
    avail <- total - sum(lower)
    if (avail <= 0) return(matrix(rep(lower, each = n), nrow = n))
    out <- matrix(NA_real_, n, k)
    i <- 1L
    while (i <= n) {
      g <- rgamma(k, 1, 1)
      w <- g / sum(g)
      x <- lower + avail * w
      if (all(x <= upper + 1e-12)) { out[i, ] <- x; i <- i + 1L }
    }
    out
  }

  lower <- c(0.10, 0.20, 0.05)
  upper <- c(0.60, 0.70, 0.50)
  total <- 1.0
  ABC   <- sample_trunc_dirichlet(n, lower, upper, total)
  A <- ABC[, 1]; B <- ABC[, 2]; C <- ABC[, 3]
  X <- runif(n)
  F <- factor(sample(c("red", "blue"), n, replace = TRUE))
  y <- 2 + 3*A + 1.5*B + 1.2*C + 0.5*X + 1*(F == "red") + rnorm(n, sd = 0.3)
  dat <- data.frame(y = y, A = A, B = B, C = C, X = X, F = F)

  mix_spec <- list(list(
    vars  = c("A", "B", "C"),
    lower = lower,
    upper = upper,
    total = total
  ))

  # Parallel significance test (default relaxed = FALSE)
  res <- svem_significance_test_parallel(
    y ~ A + B + C + X + F,
    data           = dat,
    mixture_groups = mix_spec,
    glmnet_alpha   = c(1),
    weight_scheme  = "SVEM",
    objective      = "auto",
    auto_ratio_cutoff = 1.3,
    relaxed        = FALSE,   # default, shown for clarity
    nCore          = 2,
    seed           = 123,
    verbose        = FALSE
  )
  print(res$p_value)

SVEMnet documentation built on Sept. 9, 2025, 5:38 p.m.