Define the opposite one of the two scalar functions that are maximized when running the E-M algorithm for CUBE models with covariates for feeling, uncertainty and overdispersion.
Quno(bet, esterno1)Vector of initial estimates of parameters for the uncertainty component
Matrix binding together the column vector of the posterior probabilities that each observed rating has been generated by the first component distribution of the mixture, with the matrix YY of explicative variables for the uncertainty component, expanded with a unitary vector in the first column to consider also an intercept term
It is iteratively called as an argument of "optim" within CUBE function (with covariates) as the function to minimize to compute the maximum likelihood estimates for the feeling and the overdispersion components.