# residuals.bamlss

0th

Percentile

##### Compute BAMLSS Residuals

Function to compute quantile and response residuals.

Keywords
models, regression
##### 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?

##### 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.

bamlss, predict.bamlss, fitted.bamlss.

##### Aliases
• residuals.bamlss
• plot.bamlss.residuals
##### 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)
# }

Documentation reproduced from package bamlss, version 1.1-2, License: GPL-2 | GPL-3

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