residuals.bamlss.Rd
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, ...)
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 qq-plot 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
# 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) # }