R: Update and Re-fit a GAMLSS Model

update.gamlss {gamlss} | R Documentation

## Update and Re-fit a GAMLSS Model

### Description

`update.gamlss` is the GAMLSS specific method for the generic function `update` which updates and (by default) refits a GAMLSS model.

### Usage

    ## S3 method for class 'gamlss'
    update(object, formula., ...,
                   what = c("mu", "sigma", "nu", "tau", "All"),
                   parameter= NULL, evaluate = TRUE)

### Arguments

`object` | a GAMLSS fitted model
`formula.` | the formula to update
`...` | for updating argument in `gamlss()`
`what` | the parameter in which the formula needs updating for example "mu", "sigma", "nu" "tau" or "All". If "All" all the formulae are updated. Note that the `what` argument has an effect only if only if the argument `formula.` is set
`parameter` | equivalent to `what`
`evaluate` | whether to evaluate the call or not

### Value

Returns a GAMLSS call or fitted object.

### Author(s)

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, 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`, `plot.gamlss`, `deviance.gamlss`, `formula.gamlss`

### Examples

    data(aids)
    # fit a poisson model
    h.po <-gamlss(y~pb(x)+qrt, family=PO, data=aids)
    # update with a negative binomial
    h.nb <-update(h.po, family=NBI)
    # update the smoothing
    h.nb1 <-update(h.nb,~cs(x,8)+qrt)
    # remove qrt
    h.nb2 <-update(h.nb1,~.-qrt)
    # put back qrt take log of y and fit a normal distribution
    h.nb3 <-update(h.nb1,log(.)~.+qrt, family=NO)
    # verify that it is the same
    h.no<-gamlss(log(y)~cs(x,8)+qrt,data=aids )

[Package gamlss version 5.3-2 Index]