`coef.bamlss.Rd`

Methods to extract coefficients of fitted `bamlss`

objects, either coefficients
returned from optimizer functions, or samples from a sampler functions.

Method `confint.bamlss()`

produces credible intervals or parameter samples
using quantiles.

```
# S3 method for bamlss
coef(object, model = NULL, term = NULL,
FUN = NULL, parameters = NULL,
pterms = TRUE, sterms = TRUE,
hyper.parameters = TRUE, list = FALSE,
full.names = TRUE, rescale = FALSE, ...)
# S3 method for bamlss
confint(object, parm, level = 0.95,
model = NULL, pterms = TRUE, sterms = FALSE,
full.names = FALSE, hyper.parameters = FALSE, ...)
```

- object
An object of class

`"bamlss"`

- model
Character or integer. For which model should coefficients be extracted?

- term
Character or integer. For which term should coefficients be extracted?

- FUN
A function that is applied on the parameter samples.

- parameters
If is set to

`TRUE`

, additionally adds estimated parameters returned from an optimizer function (if available).- pterms
Should coefficients of parametric terms be included?

- sterms
Should coefficients of smooths terms be included?

- hyper.parameters
For smooth terms, should hyper parameters such as smoothing variances be included?

- list
Should the returned object have a list structure for each distribution parameter?

- full.names
Should full names be assigned, indicating whether a term is parametric "p" or smooth "s".

- rescale
Should parameters of the linear parts be rescaled if the

`scale.d`

argument in`bamlss.frame`

is set to`TRUE`

.- parm
Character or integer. For which term should coefficients intervals be extracted?

- level
The credible level which defines the lower and upper quantiles that should be computed from the samples.

- ...
Arguments to be passed to

`FUN`

and function`samples`

.

Depending on argument `list`

and the number of distributional parameters, either a

`list`

or vector/matrix of model coefficients.

```
if (FALSE) ## 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)
#> Error in eval(expr, envir, enclos): object 'd' not found
## Extract coefficients based on MCMC samples.
coef(b)
#> Error in eval(expr, envir, enclos): object 'b' not found
## Now only the mean.
coef(b, FUN = mean)
#> Error in eval(expr, envir, enclos): object 'b' not found
## As list without the full names.
coef(b, FUN = mean, list = TRUE, full.names = FALSE)
#> Error in eval(expr, envir, enclos): object 'b' not found
## Coefficients only for "mu".
coef(b, model = "mu")
#> Error in eval(expr, envir, enclos): object 'b' not found
## And "s(x2)".
coef(b, model = "mu", term = "s(x2)")
#> Error in eval(expr, envir, enclos): object 'b' not found
## With optimizer parameters.
coef(b, model = "mu", term = "s(x2)", parameters = TRUE)
#> Error in eval(expr, envir, enclos): object 'b' not found
## Only parameteric part.
coef(b, sterms = FALSE, hyper.parameters = FALSE)
#> Error in eval(expr, envir, enclos): object 'b' not found
## For sigma.
coef(b, model = "sigma", sterms = FALSE,
hyper.parameters = FALSE)
#> Error in eval(expr, envir, enclos): object 'b' not found
## 95 perc. credible interval based on samples.
confint(b)
#> Error in eval(expr, envir, enclos): object 'b' not found
```