# fitted.bamlss

##### BAMLSS Fitted Values

Function to compute fitted values for `bamlss`

models. The function calls
`predict.bamlss`

to compute fitted values from samples.

- Keywords
- models, regression

##### Usage

```
# S3 method for bamlss
fitted(object, model = NULL, term = NULL,
type = c("link", "parameter"), samples = TRUE,
FUN = c95, nsamps = NULL, ...)
```

##### Arguments

- object
An object of class

`"bamlss"`

- model
Character or integer, specifies the model for which fitted values should be computed.

- term
Character or integer, specifies the model terms for which fitted values are required. Note that if

`samples = TRUE`

, e.g.,`term = c("s(x1)", "x2")`

will compute the combined fitted values`s(x1) + x2`

.- type
If

`type = "link"`

the predictor of the corresponding`model`

is returned. If`type = "parameter"`

fitted values on the distributional parameter scale are returned.- samples
Should fitted values be computed using samples of parameters or estimated parameters as returned from optimizer functions (e.g., function

`bfit`

returns`"fitted.values"`

). The former results in a call to`predict.bamlss`

, the latter simply extracts the`"fitted.values"`

of the`bamlss`

object and is not model term specific.- FUN
A function that should be applied on the samples of predictors or parameters, depending on argument

`type`

.- nsamps
If the fitted

`bamlss`

object contains samples of parameters, computing fitted values may take quite some time. Therefore, to get a first feeling it can be useful to compute fitted values only based on`nsamps`

samples, i.e.,`nsamps`

specifies the number of samples which are extracted on equidistant intervals.- …
Arguments passed to function

`predict.bamlss`

.

##### Value

Depending on arguments `model`

, `FUN`

and the structure of the `bamlss`

model, a list of fitted values or simple vectors or matrices of fitted values.

##### See Also

##### Examples

```
# NOT RUN {
## Generate some data.
d <- GAMart()
## Model formula.
f <- list(
num ~ s(x1) + s(x2) + s(x3) + te(lon,lat),
sigma ~ s(x1) + s(x2) + s(x3) + te(lon,lat)
)
## Estimate model.
b <- bamlss(f, data = d)
## Fitted values returned from optimizer.
f1 <- fitted(b, model = "mu", samples = FALSE)
## Fitted values returned from sampler.
f2 <- fitted(b, model = "mu", samples = TRUE, FUN = mean)
plot(f1, f2)
# }
```

*Documentation reproduced from package bamlss, version 1.1-2, License: GPL-2 | GPL-3*