TLE {tlemix} | R Documentation |
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.
TLE(formula,family,data,kStar=NULL, kTrim=NULL, nit = 10, msglvl = 0, result = NULL, cit = 9, test = NULL,nc, Density, Estimate, ...)
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 |
Returns an object of class TLE
.
P. Neytchev, P. Filzmoser, R. Patnaik, A. Eisl and R. Boubela, <P.Filzmoser@tuwien.ac.at> http://www.statistik.tuwien.ac.at/public/filz/
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.
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)