CompR (version 1.0)

C_piBTL: Estimation of Bradley's scores

Description

Returns the Bradley's scores of the different items and the value of the LogLikelihood

Usage

C_piBTL(Matpair, Constraint=0, eps1=1e-04, Pi=NULL, TestPi=FALSE, Zht=NULL)

Arguments

Matpair

Matrix of the cumulative sum of the results of paired comparisons or 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 is Constraint=0)

eps1

value to take into account for the convergence criteria of the algorithm of Bradley's scores estimation.(default is eps1=1e-04)

Pi

Initial values for Bradley's scores. If Pi=NULL the initialisation is based on a mean score for each item obtained from the data Matpair. Else,initial values for Bradley's scores are Pi given by the user.(default is Pi=NULL)

TestPi

Indicate if the user wants to perform a multiple comparison tests on the Bradley's scores. (default TestPi=FALSE)

Zht

Indicate the individuals probabilities to belong to the different classes. Zht has not to be provided for external use of this function. It is used in the main function EstimBradley (default Zht=NULL)

Value

List of following components:

Pi

Bradley's scores

lnL

value of the log-likelihood

lvrHO

value of the log-likelihood under the hypothesis of equal values for the Bradley's scores

lvrH1

value of the log-likelihood at the end of the Bradley's scores estimation algorithm

lRatio

value of the likelihood ration statistic

Pvalue

Pvalue of the test

H1

logical value, FALSE if Bradley's scores should be considered as equal, TRUE otherwise

VarcovPi

Matrix of covariances of Bradley's scores

restestij

Matrix of the following elements - products i and j compared - value of the test statistic - Pvalue of the test - decision at a 0.05 level

Details

The algorithm is based on a maximum likelihood approach using Dykstra method.

Examples

  data(Cocktail_Cum)
  res<-C_piBTL(Cocktail_Cum,Constraint=0,eps1=1e-04,Pi=NULL,TestPi=TRUE)
  res