# Coefficient Nonzero Percentages (SVEM) 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. ```r svem_nonzero(object, tol = 1e-07, plot = TRUE, print_table = TRUE, ...) ``` ## Arguments - `object`: An object of class `svem_model` with a non-empty `$coef_matrix` component. `svem_nonzero()` is not defined for `svem_cv` objects. - `tol`: Numeric tolerance for "nonzero". Coefficients with `|beta| > tol` are counted as nonzero. Default is `1e-7`. - `plot`: Logical; if `TRUE`, draws a quick `ggplot2` summary plot of the nonzero percentages (default `TRUE`). - `print_table`: Logical; if `TRUE`, prints a compact table of nonzero percentages to the console (default `TRUE`). - `...`: Unused; included for future extension. ## Returns Invisibly returns a data frame with columns: * `Variable`: coefficient name (excluding the intercept). * `Percent of Bootstraps Nonzero`: percentage (0–100) of bootstrap fits in which `|beta| \> tol`. If no non-intercept coefficients are found (for example, if only the intercept is present), an empty data frame is returned and a message is issued. ## Details This function expects `object$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 ```r ## ---------- 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) ``` ## See Also `coef.svem_model` for ensemble-averaged (optionally debiased) coefficients.

svem_nonzero function