residuals.bamlss {bamlss}  R Documentation 
Function to compute quantile and response residuals.
## S3 method for class 'bamlss' residuals(object, type = c("quantile", "response"), nsamps = NULL, ...) ## S3 method for class 'bamlss.residuals' plot(x, which = c("histresid", "qqresid", "wp"), spar = TRUE, ...)
object 
An object of class 
type 
The type of residuals wanted, possible types are

nsamps 
If the fitted 
x 
Object returned from function 
which 
Should a histogram with kernel density estimates be plotted, a qqplot or a worm plot? 
spar 
Should graphical parameters be set by the plotting function? 
... 
For function 
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.
A vector of residuals.
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
bamlss
, predict.bamlss
, fitted.bamlss
.
## 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) ## End(Not run)