Auxiliary function as user interface for gamlss fitting. Typically
only used when calling gamlss function with the option control.
gamlss.control(c.crit = 0.001, n.cyc = 20, mu.step = 1, sigma.step = 1, nu.step = 1,
tau.step = 1, gd.tol = Inf, iter = 0, trace = TRUE, autostep = TRUE,
save = TRUE, ...)A list with the arguments as components.
the convergence criterion for the algorithm
the number of cycles of the algorithm
the step length for the parameter mu
the step length for the parameter sigma
the step length for the parameter nu
the step length for the parameter tau
global deviance tolerance level (set more recently to Inf to allow the algorithm to conversed even if the global deviance change dramatically during the iterations)
starting value for the number of iterations, typically set to 0 unless the function refit is used
whether to print at each iteration (TRUE) or not (FALSE)
whether the steps should be halved automatically if the new global deviance is greater that the old one,
the default is autostep=TRUE
save=TRUE, (the default), saves all the information on exit.
save=FALSE saves only limited information as the global deviance and AIC.
For example fitted values, design matrices and additive terms are not saved.
The latest is useful when gamlss() is called several times within a procedure.
for extra arguments
Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby
The step length for each of the parameters mu, sigma, nu or tau is very useful to aid convergence
if the parameter has a fully parametric model.
However using a step length is not theoretically justified if the model for the parameter includes one or more smoothing terms,
(even thought it may give a very approximate result).
The c.crit can be increased to speed up the convergence especially for a large set of data which takes longer to fit.
When `trace' is TRUE, calls to the function cat produce the output for each outer iteration.
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/).
gamlss
data(aids)
h<-gamlss(y~poly(x,3)+qrt, family=PO, data=aids) #
con<-gamlss.control(mu.step=0.1)
h<-gamlss(y~poly(x,3)+qrt, family=PO, data=aids, control=con) #
rm(h,con)
Run the code above in your browser using DataLab