gamlssML {gamlss}R Documentation

Maximum Likelihood estimation of a simple GAMLSS model

Description

This is a function for fitting a gamlss.family distribution to single data set using a non linear maximisation algorithm in R. This is relevant only when there are not explanatory variables.

Usage

gamlssML(y, family = NO, weights = NULL, mu.start = NULL, 
 sigma.start = NULL, nu.start = NULL, tau.start = NULL, 
 mu.fix = FALSE, sigma.fix = FALSE, nu.fix = FALSE, 
 tau.fix = FALSE, data = NULL, start.from = NULL, ...)

Arguments

y

a vector of data requiring the fit of a gamlss.family distribution or a formula, for example, y~1 (explanatory variables are ignored).

family

gamlss.family object, which is used to define the distribution and the link functions of the various parameters. The distribution families supported by gamlssML() can be found in gamlss.family

weights

a vector of weights. Here weights can be used to weight out observations (like in subset) or for a weighted likelihood analysis where the contribution of the observations to the likelihood differs according to weights. The length of weights must be the same as the number of observations in the data. By default, the weight is set to one. To set weights to vector w use weights=w

mu.start

a scalar of initial values for the location parameter mu e.g. mu.start=4

sigma.start

a scalar of initial values for the scale parameter sigma e.g. sigma.start=1

nu.start

scalar of initial values for the parameter nu e.g. nu.start=3

tau.start

scalar of initial values for the parameter tau e.g. tau.start=3

mu.fix

whether the mu parameter should be kept fixed in the fitting processes e.g. mu.fix=FALSE

sigma.fix

whether the sigma parameter should be kept fixed in the fitting processes e.g. sigma.fix=FALSE

nu.fix

whether the nu parameter should be kept fixed in the fitting processes e.g. nu.fix=FALSE

tau.fix

whether the tau parameter should be kept fixed in the fitting processes e.g. tau.fix=FALSE

data

a data frame containing the variable y. If this is missing, the variable should be on the search list. e.g. data=aids

start.from

a gamlss object to start from the fitting or vector of length as many parameters in the distribution

...

for extra arguments

Details

This function which fits a gamlss.family distribution to a single data set is using a non linear maximisation. in fact it uses the internal function MLE() which is a copy of the mle() function of package stat4. The function gamlssML() could be for large data faster than the equivalent gamlss() function which is designed for regression type of models.

Value

Returns a gamlssML object which behaves like a gamlss fitted objected

Author(s)

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby r.rigby@londonmet.ac.uk, Vlasis Voudouris v.voiudouris@londonmet.ac.uk and Majid Djennad m.djennad.londonmet.ac.uk

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. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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.

See Also

gamlss.family, gamlss

Examples

#-------- negative binomial 1000 observations
y<- rNBI(1000)
 system.time(m1<-gamlss(y~1, family=NBI))
  system.time(m1a<-gamlss(y~1, family=NBI, trace=FALSE))
system.time(m11<-gamlssML(y, family=NBI))
AIC(m1,m1a,m11, k=0)
# neg. binomial   n=10000
 y<- rNBI(10000)
 system.time(m1<-gamlss(y~1, family=NBI))
  system.time(m1a<-gamlss(y~1, family=NBI, trace=FALSE))
system.time(m11<-gamlssML(y, family=NBI))
AIC(m1,m1a,m11, k=0)
# binomial type data 
data(aep)
m1 <- gamlssML(aep$y, family=BB) # ok
m2 <- gamlssML(y, data=aep, family=BB) # ok
m3 <- gamlssML(y~1, data=aep, family=BB) # ok but not 
#gamlssML(aep$y~1, family=BB)
[Package gamlss version 4.3-5 Index]