gamlss.mx-package: The GAMLSS add on package for mixture distributions

Description

The main purpose of this package is to allow the user of the GAMLSS models to fit mixture distributions.

Arguments

Author

Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk> and Bob Rigby <r.rigby@londonmet.ac.uk>

Maintainer: Mikis Stasinopoulos <mikis.stasinopoulos@gamlss.org>

Details

Package:	gamlss.mx
Type:	Package
Version:	0.0
Date:	2005-08-3
License:	GPL (version 2 or later)

This package has two main function the gamlssMX() which is loosely based on the package flexmix of R and the function gamlssNP() which is based on the npmlreg package of Jochen Einbeck, Ross Darnell and John Hinde (2006) which in turns is based on several GLIM4 macros originally written by Murray Aitkin and Brian Francis. It also contains the function gqz() which is written by Nick Sofroniou and the function gauss.quad() written by Gordon Smyth.

References

Jochen Einbeck, Ross Darnell and John Hinde (2006) npmlreg: Nonparametric maximum likelihood estimation for random effect models, R package version 0.34

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.

Stasinopoulos M.D., Kneib T, Klein N, Mayr A, Heller GZ. (2024) Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications. Cambridge University Press.

(see also https://www.gamlss.com/).

Examples

Run this code

data(enzyme)
mmNO <- gamlssMX(act~1, family=NO, K=2, data=enzyme)
mmNO
# \donttest{
# also to make sure that it reaches the maximum
mmNOs <- gamlssMXfits(n=10, act~1, family=NO, K=2, data=enzyme)
fyNO<-dMX(y=seq(0,3,.01), mu=list(1.253, 0.1876), sigma=list(exp(-0.6665 ), exp(-2.573 )),
                  pi=list(0.4079609, 0.5920391 ), family=list("NO","NO") )
hist(enzyme$act,freq=FALSE,ylim=c(0,3.5),xlim=c(0,3),br=21)
lines(seq(0,3,.01),fyNO, col="red")
# equivalent model using gamlssNP
mmNP <- gamlssNP(act~1, data=enzyme, random=~1,sigma.fo=~MASS,family=NO, K=2)
# }