svem_nonzero: Coefficient Nonzero Percentages (SVEM)

Description

Summarizes variable-selection stability across SVEM bootstrap refits by computing the percentage of bootstrap iterations in which each coefficient (excluding the intercept) is nonzero, using a small tolerance. Optionally produces a quick ggplot2 summary and/or prints a compact table.

Usage

svem_nonzero(object, tol = 1e-07, plot = TRUE, print_table = TRUE, ...)

Value

Invisibly returns a data frame with columns:

Variable: coefficient name (excluding the intercept).
Percent of Bootstraps Nonzero: percentage (0–100) of bootstrap fits in which |beta| > tol.

If no non-intercept coefficients are found (for example, if only the intercept is present), an empty data frame is returned and a message is issued.

Arguments

object: An object of class svem_model with a non-empty $coef_matrix component. svem_nonzero() is not defined for svem_cv objects.
tol: Numeric tolerance for "nonzero". Coefficients with |beta| > tol are counted as nonzero. Default is 1e-7.
plot: Logical; if TRUE, draws a quick ggplot2 summary plot of the nonzero percentages (default TRUE).
print_table: Logical; if TRUE, prints a compact table of nonzero percentages to the console (default TRUE).
...: Unused; included for future extension.

Details

This function expects object$coef_matrix to contain the per-bootstrap coefficients (including an intercept column), typically created by SVEMnet when save_boot = TRUE (or similar) is enabled. Rows correspond to bootstrap fits; columns correspond to coefficients.

Internally, svem_nonzero():

checks for and drops rows of coef_matrix that contain any non-finite values, to keep summaries stable;
drops an "(Intercept)" column if present;
computes 100 * mean(|beta_j| > tol) across bootstrap rows for each remaining coefficient.

The plot is a simple line + point chart with labels, ordered by decreasing nonzero percentage. It is intended as a quick diagnostic; for publication graphics, you may want to customize the output data frame with your own plotting code.

Examples

Run this code

# \donttest{
  ## ---------- Gaussian demo ----------
  set.seed(10)
  n  <- 220
  x1 <- rnorm(n)
  x2 <- rnorm(n)
  x3 <- rnorm(n)
  eps <- rnorm(n, sd = 0.4)
  y   <- 0.7 + 1.5*x1 - 0.8*x2 + 0.05*x3 + eps
  dat <- data.frame(y, x1, x2, x3)

  fit <- SVEMnet(y ~ (x1 + x2 + x3)^2, data = dat,
                 nBoot = 40, relaxed = TRUE)

  # Table + plot of bootstrap nonzero percentages
  nz <- svem_nonzero(fit, tol = 1e-7, plot = TRUE, print_table = TRUE)
  head(nz)

  ## ---------- Binomial demo ----------
  set.seed(11)
  n  <- 260
  x1 <- rnorm(n)
  x2 <- rnorm(n)
  x3 <- rnorm(n)
  lp <- -0.3 + 0.9*x1 - 0.6*x2 + 0.2*x3
  p  <- 1/(1+exp(-lp))
  y  <- rbinom(n, 1, p)
  dat_b <- data.frame(y, x1, x2, x3)

  fit_b <- SVEMnet(y ~ x1 + x2 + x3, data = dat_b,
                   family = "binomial", nBoot = 40, relaxed = TRUE)

  # Still summarizes bootstrap selection frequencies for binomial fits
  svem_nonzero(fit_b, plot = TRUE, print_table = TRUE)
# }