plot.mmeln,logLik.mmeln,anova.mmeln,print.mmeln {mmeln}

Utility methods for objects of class mmeln

Description

Methods to plot, compare and assessed the log(Likelihood) of objects of class mmeln. The method cov.tsf which convert a vector of covariance parameter into a covariance matrix and multnm which performs an estimation of multinomial model are internal methods that should not be used unless by experimented user

Usage

## S3 method for class 'mmeln'
plot(x,...,main="",xlab="Temps",ylab="Y",col=1:x$G,leg=TRUE)
## S3 method for class 'mmeln'
logLik(object,...,param=NULL)
## S3 method for class 'mmeln'
anova(object, ..., test = TRUE)
## S3 method for class 'mmelnSOL'
print(x,...,se.estim="MLR")
cov.tsf(param,type,p)

Arguments

x

An object of type mmeln or mmelnSOL (mmelnSOL required for the command print)

object

An object of type mmeln

main

Title of the graphic

xlab

Label of the X axis

ylab

label of the Y axis

col

Colour of the lines plotted in each group

leg

Logical value indicating if the legend is plotted or not

...

other object of type mmeln to compare (use is only valid in the anova command)

test

logical value indicating if the likelihood ratio test is required. Valid only when two objects are entered

param

For the function logLik a list of parameters like defined in mmeln, by default it is taken from the mmeln object. For the cov.tsf function it is vector containing the distinct value of the covariance as defined in the mmeln function

type

Type of covariance as defined in mmeln

p

Rank of covariance matrix

se.estim

Type of estimator. The default is MLR based on the information matrix define as Ir^(-1)=I^(-1)IeI^(-1). The other choices are the Observational information matrix "ML" and the Empirical information matrix based on the cross product of the gradient of the logLikehood "ML.E"

Details

The function plot draws X$G lines showing the expected value. The function logLik gives the log(Likelihood) of a model. The function anova compares mmeln models and gives the total number of parameters, the log(Likelihood), the AIC (Akaike information criterion), the BIC (Bayesian information criterion based on the number of observation) and the BIC2 (BIC based on the number of subjects). Optionally, the Likelihood ratio test is performed. The function print is used for solution given by the estim.mmeln function. The print method gives the number of iterations required for convergence and the statistics for the location, mixture and covariance parameters.

Author(s)

Charles-Édouard Giguère

References

McLachlan, G. & Peel, D. (2000), Finite mixture models,Wiley

Bernard D. Flury (1997), A first course in multivariate statistics, Springer

Pinheiro José C. & Bates Douglas M. (2000), Mixed-Effects Models in S and S-PLUS, Springer

M.S. Srivastava (2002), Methods of Multivariate Statistics, WILEY

See Also

mmeln

Examples

#### load an example.
data(exY)

### estimation of the parameters of the mixture
temps=1:3
mmeln1=mmeln(Y,G=2,form.loc=~factor(temps)-1,form.mel=~1,cov="CS")
mmeln2=mmeln(Y,G=2,form.loc=list(~temps,~I((temps-2)^2)),form.mel=~1,cov="CS")

mix1=estim(mmeln1,mu=list(rep(1,3),rep(2,3)),tau=c(0)
          ,sigma=list(c(1,.4),c(1,.4)),iterlim=100,tol=1e-6)

mix2=estim(mmeln2,mu=list(c(2,1),c(5,-1)),tau=c(0)
          ,sigma=list(c(1,.4),c(1,.4)),iterlim=100,tol=1e-6)


mix1
mix2

anova(mix1,mix2)
plot(mix1,main="Mixture of multivariate normal")
plot(mix2,main="Mixture of multivariate normal")