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.

Examples

if (FALSE) ## 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)
#> Error in eval(expr, envir, enclos): object 'd' not found

## Fitted values returned from optimizer.
f1 <- fitted(b, model = "mu", samples = FALSE)
#> Error in eval(expr, envir, enclos): object 'b' not found

## Fitted values returned from sampler.
f2 <- fitted(b, model = "mu", samples = TRUE, FUN = mean)
#> Error in eval(expr, envir, enclos): object 'b' not found

plot(f1, f2)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f1' not found

Arguments

Value

See also

Examples