Function to compute fitted values for `bamlss`

models. The function calls
`predict.bamlss`

to compute fitted values from samples.

# 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)
# }