probcubpq {CUB} | R Documentation |
Compute the probability distribution of a CUB model with covariates for both the feeling and the uncertainty components.
probcubpq(m,ordinal,Y,W,bet,gama)
m |
Number of ordinal categories |
ordinal |
Vector of ordinal responses |
Y |
Matrix of covariates for explaining the uncertainty component |
W |
Matrix of covariates for explaining the feeling component |
bet |
Vector of parameters for the uncertainty component, whose length equals NCOL(Y) + 1 to include an intercept term in the model (first entry) |
gama |
Vector of parameters for the feeling component, whose length equals NCOL(W)+1 to include an intercept term in the model (first entry) |
A vector of the same length as ordinal
, whose i-th component is the probability of the
i-th rating according to a CUB distribution with given covariates for both uncertainty and feeling,
and specified coefficients vectors bet
and gama
, respectively.
Piccolo D. (2006). Observed Information Matrix for MUB Models,
Quaderni di Statistica, 8, 33–78
Piccolo D. and D'Elia A. (2008). A new approach for modelling consumers' preferences, Food Quality and Preference,
18, 247–259
Iannario M. and Piccolo D. (2012). CUB models: Statistical methods and empirical evidence, in:
Kenett R. S. and Salini S. (eds.), Modern Analysis of Customer Surveys: with applications using R,
J. Wiley and Sons, Chichester, 231–258
bitgama
, probcub00
, probcubp0
, probcub0q
data(relgoods) m<-10 naord<-which(is.na(relgoods$Physician)) nacov<-which(is.na(relgoods$Gender)) na<-union(naord,nacov) ordinal<-relgoods$Physician[-na] W<-Y<-relgoods$Gender[-na] gama<-c(-0.91,-0.7); bet<-c(-0.81,0.93) probi<-probcubpq(m,ordinal,Y,W,bet,gama)