# NOT RUN {
## Simulate data.
d <- GAMart()
## Model formula.
f <- list(
num ~ s(x1) + s(x2) + s(x3),
sigma ~ s(x1) + s(x2) + s(x3)
)
## Estimate model.
b <- bamlss(f, data = d)
## Extract coefficients based on MCMC samples.
coef(b)
## Now only the mean.
coef(b, FUN = mean)
## As list without the full names.
coef(b, FUN = mean, list = TRUE, full.names = FALSE)
## Coefficients only for "mu".
coef(b, model = "mu")
## And "s(x2)".
coef(b, model = "mu", term = "s(x2)")
## With optimizer parameters.
coef(b, model = "mu", term = "s(x2)", parameters = TRUE)
## Only parameteric part.
coef(b, sterms = FALSE, hyper.parameters = FALSE)
## For sigma.
coef(b, model = "sigma", sterms = FALSE,
hyper.parameters = FALSE)
## 95 perc. credible interval based on samples.
confint(b)
# }
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