TLE {tlemix}R Documentation

Trimmed Likelihood Estimator

Description

TLE implements a general framework for robust fitting of finite mixture models. Parameter estimation is performed using the EM algorithm.

Currently two model drivers are inluded: flexmix.Density (flexmix.Enstimate) for gaussian, poisson and binomial regression models and FLXmclust.Density (FLXmclust.Estimate) for model based clustering.

Usage

        TLE(formula,family,data,kStar=NULL, kTrim=NULL, nit = 10, msglvl = 0, result = NULL, cit = 9, test = NULL,nc, Density, Estimate, ...)

Arguments

formula An object of class formula.
family The family to be used.
data Data frame containing the x and y variables with an optional attribute family being either gaussian,poisson or binomial
kStar k*- size of the initial random subsample
kTrim Trimming parameter: size of the C-steps random subsample
nit Number of iterations
msglvl Level of messages
result Restart/continuation information
cit Number of iterations in refinement step
test Expected true loglikelihood of the model; procedure will be stopped if reached.
nc Number of components.
Density Density function of type - function(data,solution,model,family,...)
Estimate Specific estimation procedure interface: function(data,ind,model,family,...)
... Arguments to be passed to methods Estimate and Density

Value

Returns an object of class TLE.

Author(s)

P. Neytchev, P. Filzmoser, R. Patnaik, A. Eisl and R. Boubela, <P.Filzmoser@tuwien.ac.at> http://www.statistik.tuwien.ac.at/public/filz/

References

N. Neykov, P. Filzmoser, R. Dimova, and P. Neytchev. Robust fitting of mixtures using the trimmed likelihood estimator. Computational Statistics and Data Analysis, Vol. 17(3), pp. 299-308, 2007.

See Also

flexmix

Examples


data(gaussData)
est.tle = TLE(y~x,"gaussian",data=gaussData,nit=4, msglvl=1, cit=3, Density=flexmix.Density, Estimate=flexmix.Estimate, nc=2)

# Plot the 2-dimensional data                   
tleplot(est.tle, gaussData)


[Package tlemix version 0.0.4 Index]