TLE: Trimmed Likelihood Estimator

In tlemix: Trimmed Maximum Likelihood Estimation

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=1, 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, <[e-mail]> 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)
# This example takes some seconds:
#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)

tlemix documentation built on May 2, 2019, 5:57 a.m.