bamlss (version 1.1-2)

residuals.bamlss: Compute BAMLSS Residuals

Description

Function to compute quantile and response residuals.

Usage

# S3 method for bamlss
residuals(object, type = c("quantile", "response"),
  nsamps = NULL, ...)
# S3 method for bamlss.residuals
plot(x, which = c("hist-resid", "qq-resid"),
  spar = TRUE, ...)

Arguments

object

An object of class "bamlss".

type

The type of residuals wanted, possible types are "quantile" residuals and "response" residuals.

nsamps

If the fitted bamlss object contains samples of parameters, computing residuals may take quite some time. Therefore, to get a first feeling it can be useful to compute residuals only based on nsamps samples, i.e., nsamps specifies the number of samples which are extracted on equidistant intervals.

x

Object returned from function residuals.bamlss().

which

Should a histogram with kernel density estimates be plotted or a qq-plot?

spar

Should graphical parameters be set by the plotting function?

…

For function residuals.bamlss() arguments passed to possible $residuals() functions that may be part of a bamlss.family. For function plot.bamlss.residuals() arguments passed to function hist.default and qqnorm.default.

Value

A vector of residuals.

Details

Response residuals are the raw residuals, i.e., the response data minus the fitted distributional mean. If the bamlss.family object contains a function $mu(par, …), then raw residuals are computed with y - mu(par) where par is the named list of fitted values of distributional parameters. If $mu(par, ...) is missing, then the fitted values of the first distributional parameter are used.

Randomized quantile residuals are based on the cumulative distribution function of the bamlss.family object, i.e., the $p(y, par, ...) function.

References

Dunn P. K., and Smyth G. K. (1996). Randomized Quantile Residuals. Journal of Computational and Graphical Statistics 5, 236-244.

See Also

bamlss, predict.bamlss, fitted.bamlss.

Examples

# NOT RUN {
## Generate data.
d <- GAMart()

## Estimate models.
b1 <- bamlss(num ~ s(x1), data = d)
b2 <- bamlss(num ~ s(x1) + s(x2) + s(x3), data = d)

## Extract quantile residuals.
e1 <- residuals(b1, type = "quantile")
e2 <- residuals(b2, type = "quantile")

## Plots.
plot(e1)
plot(e2)
# }