smooth_check: MCMC Based Simple Significance Check for Smooth Terms

In bamlss: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

MCMC Based Simple Significance Check for Smooth Terms

Description

For each smooth term estimated with MCMC, the function computes 95 intervals and simply computes the fraction of the cases where the interval does not contain zero.

Usage

smooth_check(object, newdata = NULL, model = NULL, term = NULL, ...)

Arguments

object	A fitted model object which contains MCMC samples.
newdata	Optionally, use new data for computing the check.
model	Character, for which model should the check be computed?
term	Character, for which term should the check be computed?
...	Arguments passed to predict.bamlss.

Examples

## Not run: ## Simulate some data.
d <- GAMart()

## Model formula.
f <- list(
  num ~ s(x1) + s(x2) + s(x3),
  sigma ~ s(x1) + s(x2) + s(x3)
)

## Estimate model with MCMC.
b <- bamlss(f, data = d)

## Run the check, note that all variables
## for sigma should have no effect.
smooth_check(b)

## End(Not run)

bamlss documentation built on Sept. 26, 2025, 3 p.m.