gamlss (version 5.1-4)

residuals.gamlss: Extract Residuals from GAMLSS model

Description

residuals.gamlss is the GAMLSS specific method for the generic function residuals which extracts the residuals for a fitted model. The abbreviated form resid is an alias for residuals.

Usage

# S3 method for gamlss
residuals(object, what = c("z-scores", "mu", "sigma", "nu", "tau"), 
                         type = c("simple", "weighted", "partial"), 
                         terms=NULL, ...)

Arguments

object

a GAMLSS fitted model

what

specify whether the standardized residuals are required, called here the "z-scores", or residuals for a specific parameter

type

the type of residual if residuals for a parameter are required

terms

if type is "partial" this specifies which term is required

…

for extra arguments

Value

a vector or a matrix of the appropriate residuals of a GAMLSS model. Note that when weights are used in the fitting the length of the residuals can be different from N the length of the fitted values. Observations with weights equal to zero are not appearing in the residuals. Also observations with frequencies as weights will appear more than once according to their frequencies.

Details

The "z-scores" residuals saved in a GAMLSS object are the normalized (randomized) quantile residuals (see Dunn and Smyth, 1996). Randomization is only needed for the discrete family distributions, see also rqres.plot. Residuals for a specific parameter can be "simple" = (working variable - linear predictor), "weighted"= sqrt(working weights)*(working variable - linear predictor) or "partial"= (working variable - linear predictor)+contribution of specific terms.

References

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also http://www.gamlss.org/).

See Also

print.gamlss, summary.gamlss, fitted.gamlss, coef.gamlss, residuals.gamlss, update.gamlss, plot.gamlss, deviance.gamlss, formula.gamlss

Examples

# NOT RUN {
data(aids)
h<-gamlss(y~poly(x,3)+qrt, family=NBI, data=aids) # 
plot(aids$x,resid(h))
plot(aids$x,resid(h,"sigma") )
rm(h)
# }