get_B_verification_examples: Verification Examples for get_B

In lgspline: Lagrangian Multiplier Smoothing Splines for Smooth Function Estimation

Verification Examples for get_B

Description

Simple, self-contained examples that reviewers can run to verify that get_B produces correct output. These exercise Path 2 (Gaussian, no correlation) and Path 3 (binomial GLM).

Examples

## Not run: 
## Example 1: Path 2 - Gaussian identity, with knots
set.seed(1234)
t <- runif(200, -5, 5)
y <- sin(t) + rnorm(200, 0, 0.5)
fit1 <- lgspline(t, y, K = 3, opt = FALSE, wiggle_penalty = 1e-4)
stopifnot(inherits(fit1, "lgspline"))
stopifnot(length(fit1$B) == 4)  # K+1 = 4 partitions
cat("Example 1 passed: Gaussian identity, K=3\n")

preds1 <- predict(fit1, new_predictors = rnorm(10))
stopifnot(all(is.finite(preds1)))
cat("  Predictions finite: OK\n")

## Example 2: Path 2 - Gaussian identity, K=0 (no constraints)
fit2 <- lgspline(t, y, K = 0, opt = FALSE, wiggle_penalty = 1e-4)
stopifnot(inherits(fit2, "lgspline"))
stopifnot(length(fit2$B) == 1)
preds2 <- predict(fit2, new_predictors = rnorm(10))
stopifnot(all(is.finite(preds2)))
cat("Example 2 passed: Gaussian identity, K=0\n")

## Example 3: Path 3 - Binomial GLM
y_bin <- rbinom(200, 1, plogis(sin(t)))
fit3 <- lgspline(t, y_bin, K = 2, family = binomial(),
                 opt = FALSE, wiggle_penalty = 1e-3)
stopifnot(inherits(fit3, "lgspline"))
preds3 <- predict(fit3, new_predictors = rnorm(10))
stopifnot(all(preds3 >= 0 & preds3 <= 1))
cat("Example 3 passed: Binomial GLM, K=2\n")

## Example 4: Path 2 with QP constraints (monotonic increase)
t_sorted <- sort(runif(100, -3, 3))
y_mono <- t_sorted + 0.5 * sin(t_sorted) + rnorm(100, 0, 0.3)
fit4 <- lgspline(t_sorted, y_mono, K = 2,
                 qp_monotonic_increase = TRUE,
                 opt = FALSE, wiggle_penalty = 1e-4)
preds4 <- predict(fit4, new_predictors = cbind(sort(rnorm(50))))
stopifnot(all(diff(preds4) >= -sqrt(.Machine$double.eps)))
cat("Example 4 passed: Monotonic increase QP constraint\n")

## Example 5: Path 2 with range constraints
fit5 <- lgspline(t, y, K = 3,
                 qp_range_lower = -2, qp_range_upper = 2,
                 opt = FALSE, wiggle_penalty = 1e-4)
stopifnot(all(fit5$ytilde >= -2 - 0.01))
stopifnot(all(fit5$ytilde <= 2 + 0.01))
cat("Example 5 passed: Range constraints\n")

## Example 6: Multi-predictor
set.seed(1234)
x1 <- rnorm(300)
x2 <- rnorm(300)
y6 <- sin(x1) + cos(x2) + rnorm(300, 0, 0.5)
fit6 <- lgspline(cbind(x1, x2), y6, K = 4,
                 opt = FALSE, wiggle_penalty = 1e-5)
stopifnot(inherits(fit6, "lgspline"))
stopifnot(fit6$q_predictors == 2)
cat("Example 6 passed: 2D predictor, K=4\n")

## Example 7: Coefficient consistency (determinism)
set.seed(1234)
t7 <- runif(150, -4, 4)
y7 <- 2 * cos(t7) + rnorm(150, 0, 0.4)
fit7a <- lgspline(t7, y7, K = 2, opt = FALSE, wiggle_penalty = 1e-5)
set.seed(999)
fit7b <- lgspline(t7, y7, K = 2, opt = FALSE, wiggle_penalty = 1e-5)
max_diff <- max(abs(unlist(fit7a$B) - unlist(fit7b$B)))
stopifnot(max_diff < 1e-12)
cat("Example 7 passed: Deterministic coefficient reproduction\n")

cat("\nAll verification examples passed.\n")

## End(Not run)

lgspline documentation built on May 8, 2026, 5:07 p.m.