CompR (version 1.0)

EstimBradley: Estimation of Bradley's scores in the different classes of subjects

Description

Estimates Bradley's scores according the desired number of classes.

Usage

EstimBradley(Data, Constraint=0, Tcla=1, eps=1e-04, eps1=1e-04, TestPi=TRUE)

Arguments

Data

Object of class DataPairComp

Constraint

Kind of constraint on Bradley's scores. If Constraint=0, the sum of Bradley's scores should be equal to 1. For other values for Constraint, the product of Bradley's scores should be equal to 1.(default constraint=0)

Tcla

Number of classes, default=1, no segmentation.

eps

value of the convergence criteria for the EM algorithm (default eps=1e-04).

eps1

value of the criteria convergence for Dykstra algorithm (default eps1=1e-04).

TestPi

if TestPi=TRUE multiple comparison tests for Bradley's scores are performed. Else no multiple comparison test. (default is TestPi=TRUE )

Value

Object of class BradleyEstim with the following components:

Lvriter

matrix describing the evolution of log likelihood at the different steps of the maximization procedure.

Lvr

Final value of the log likelihood

Lambda

numeric Final estimates of classes' weight

Pi

list of Tcla elements containing Bradley'scores for the different criteria

Zh

matrix of the belongings probabilities of the individuals to the different classes and the belonging class according to these probabilities

IC

value of Information Criterion (AIC,BIC,CAIC)

Restestglob

(given if TestPi=TRUE) list of five elements:lvrH0 matrix of size (Tcla * number of criteria), giving the value of the log likelihood under the hypothesis of equality of Bradley's scoreslvrH1 matrix of size (Tcla * number of criteria), giving the value of the log likelihood under the hypothesis of non equality of Bradley's scoreslRatio matrix of size (Tcla * number of criteria), giving the value of the log likelihood Ratio statisticPvalue matrix of size (Tcla * number of criteria), giving the P value of the log likelihood Ratio testH1 matrix of size (Tcla * number of criteria) giving the result of rejection of equality of Bradley's scores

Restestprod

(given if TestPi=TRUE and if Bradley's scores are not equal) list of Tcla elements of type matrix of size (number of paired comparison * 7), each column corresponding to:class identification,criterion identification,product identification i,product identification j,value for the statistic corresponding to H0: equality of the Bradley's scores of products i and j,P value of this test,Rejection or acceptation of H0 for a level of 5%.

Varcov

(given if TestPi=TRUE)list of Tcla elements containing Bradley'scores covariance matrices for the different criteria.

Details

The estimation is based on maximum likelihood for mixture distributions with E.M. algorithm.

Examples

data(Cocktail)
show(Cocktail)
ResCock1<-EstimBradley(Cocktail,Constraint=0,Tcla=1,eps=0.001,eps1=0.001,TestPi=TRUE)
show(ResCock1)