gamlssMX: Function to fit finite mixture of gamlss family distributions

Description Usage Arguments Author(s) References See Also Examples

Description

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

Usage

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gamlssMX(formula = formula(data), pi.formula = ~1, 
         family = "NO", weights, K = 2, prob = NULL, 
         data = sys.parent(), 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 = sys.parent(), 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(s)

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.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2003) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

See Also

gamlss, gamlss.family

Examples

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library(MASS)
data(geyser)
# fitting 2 finite normal mixtures 
m1<-gamlssMX(waiting~1,data=geyser,family=NO, K=2)
## Not run: 
#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

## End(Not run)

mstasinopoulos/GAMLSS-Finite-Mixtures documentation built on May 27, 2019, 4:51 a.m.