R: Extract Residuals from GAMLSS model

residuals.gamlss {gamlss} | R Documentation

## 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 class '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

### 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.

### 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.

### Note

The "weighted" residuals of a specified parameter can be zero and one if the square of first derivative have been used in the fitting of this parameter

### Author(s)

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk and Bob Rigby

### 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.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

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, https://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 https://www.gamlss.com/).

### See Also

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

### Examples

    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)

[Package gamlss version 5.3-2 Index]