Quno: Auxiliary function for the log-likelihood estimation of CUBE...
In CUB: A Class of Mixture Models for Ordinal Data
Quno R Documentation
Auxiliary function for the log-likelihood estimation of CUBE models with covariates
Description
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.
Usage
Quno(bet, esterno1)
Arguments
bet
Vector of initial estimates of parameters for the uncertainty component
esterno1
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
Details
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.
CUB documentation built on May 29, 2024, 5:23 a.m.
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| Quno | R Documentation |
Auxiliary function for the log-likelihood estimation of CUBE models with covariates
Description
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.
Usage
Quno(bet, esterno1)
Arguments
bet |
Vector of initial estimates of parameters for the uncertainty component |
esterno1 |
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 |
Details
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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