cubecsi function

Main function for CUBE models with covariates only for feeling

Estimate and validate a CUBE model for ordinal data, with covariates only for explaining the feeling component.

cubecsi(m, ordinal, W, starting, maxiter, toler)

Arguments

• m: Number of ordinal categories
• ordinal: Vector of ordinal responses
• W: Matrix of selected covariates for explaining the feeling component
• starting: Vector of initial parameters estimates to start the optimization algorithm, with length equal to NCOL(W) + 3 to account for an intercept term for the feeling component (first entry)
• maxiter: Maximum number of iterations allowed for running the optimization algorithm
• toler: Fixed error tolerance for final estimates

Returns

An object of the class "CUBE". For cubecsi, $niter will return a NULL value since the optimization procedure is not iterative but based on "optim" (method = "L-BFGS-B", option hessian=TRUE).

$varmat will return the inverse of the numerically computed Hessian when it is positive definite, otherwise the procedure will return a matrix of NA entries.

See Also

loglikcubecsi, inibestcubecsi, CUBE