Bayesian GAMLSS Wrapper

Description

A wrapper around gamlss2 for fitting Bayesian GAMLSS models by MCMC sampling. Starting values are computed using maxit backfitting iterations before sampling. The controls n.iter, burnin, and thin can be specified directly; weights and offset are passed through to gamlss2.

Usage

bamlss2(formula, n.iter = 1200, burnin = 200, thin = 1, maxit = 2, ...)

Arguments

formula	A GAM-type formula or Formula. The model can include smooth terms as provided by the mgcv package.
n.iter	Integer, the total number of MCMC iterations.
burnin	Integer, the burn-in period.
thin	Integer, the thinning parameter for saved samples.
maxit	Integer, the number of backfitting iterations used to compute starting values before running MCMC.
…	Further arguments passed to gamlss2 and/or gamlss2_control.

Value

An object of class “bamlss2” inheriting from “gamlss2” containing the fitted model and posterior samples.

References

Umlauf N, Klein N, Zeileis A (2018). “BAMLSS: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)”. Journal of Computational and Graphical Statistics, 27(3), 612–627. doi:10.1080/10618600.2017.1407325

See Also

gamlss2, mcmc, BS

Examples

library("gamlss2")

## air quality data
air <- subset(airquality, !is.na(Ozone))

## set the seed
set.seed(123)

## estimate Gamma model with MCMC
m <- bamlss2(
  Ozone ~ s(Temp) + s(Wind) | s(Temp),
  data = air,
  family = GA
)

## posterior summary
summary(m)

## plot estimated effects
plot(m)

## plot samples
plot(m, which = "samples", ask = TRUE)

## predict parameters using samples
pm <- predict(m, type = "parameter", FUN = mean)
print(head(pm))
psd <- predict(m, type = "parameter", FUN = sd)
print(head(psd))