gamlssMX: Function to fit finite mixture of gamlss family distributions

Description

The function gamlssMX is design for fitting a K fold non parametric mixture of gamlss family distributions.

Usage

gamlssMX(formula = formula(data), pi.formula = ~1, 
         family = "NO", weights, K = 2, prob = NULL, 
         data, control = MX.control(...), 
         g.control = gamlss.control(trace = FALSE, ...), 
         zero.component = FALSE,   ...)
gamlssMXfits(n = 5, formula = formula(data), pi.formula = ~1, 
         family = "NO", weights, K = 2, prob = NULL, 
         data, control = MX.control(), 
         g.control = gamlss.control(trace = FALSE),
         zero.component = FALSE, ... )

Arguments

formula: This argument it should be a formula (or a list of formulea of length K) for modelling the mu parameter of the model. Note that modelling the rest of the distributional parameters it can be done by using the usual ... which passes the arguments to gamlss()
pi.formula: This should be a formula for modelling the prior probabilities as a function of explanatory variables. Note that no smoothing of other additive terms are allowed here only the usual linear terms. The modelling here is done using the multinom() function from package nnet
family: This should be a gamlss.family distribution (or a list of distributions). Note that if different distributions are used here their parameters should be comparable for ease of interpretation.
weights: prior weights if needed
K: the number of finite mixtures with default K=2
prob: prior probabilities if required for starting values
data: the data frame nedded for the fit. Note that this is compulsory if pi.formula is used.
control: This argument sets the control parameters for the EM iterations algorithm. The default setting are given in the MX.control function
g.control: This argument can be used to pass to gamlss() control parameters, as in gamlss.control
n: the number of fits required in gamlssMXfits()
zero.component: whether zero component models exist, default is FALSE
...: for extra arguments

Author

Mikis Stasinopoulos and 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.

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

library(MASS)
data(geyser)
# fitting 2 finite normal mixtures 
m1<-gamlssMX(waiting~1,data=geyser,family=NO, K=2)
# \donttest{
#fitting 2 finite gamma mixtures 
m2<-gamlssMX(waiting~1,data=geyser,family=GA, K=2)
# fitting a model for pi
# first create a data frame
geyser1<-matrix(0,ncol=2, nrow=298)
geyser1[,1] <-geyser$waiting[-1]
geyser1[,2] <-geyser$duration[-299]
colnames(geyser1)<- c("waiting", "duration")
geyser1 <-data.frame(geyser1)
# get the best of 5 fits
m3<-gamlssMXfits(n=5, waiting~1, pi.formula=~duration, data=geyser1,family=NO, K=2)
m3
# }