Compute BAMLSS Residuals

Function to compute quantile and response residuals.

# 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", "wp"),
  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, a qq-plot or a worm 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.

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.

Value

A vector of residuals.

References

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

van Buuren S., and Fredriks M. (2001) Worm Plot: Simple Diagnostic Device for Modelling Growth Reference Curves. Statistics in Medicine, 20, 1259--1277

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)
# }