tlemix (version 0.1.3)

TLE: 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=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.

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

Run this code
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)

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