gamlss (version 5.2-0)

update.gamlss: 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 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.

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

# NOT RUN {
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 )
# }

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