BAMLSS Fitted Values

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

bamlss, predict.bamlss.

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)
# }