Description Usage Arguments Value See Also
Estimate and validate a CUBE model for ordinal data, with covariates only for explaining the feeling component.
1 | cubecsi(m, ordinal, W, starting, maxiter, toler)
|
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 |
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.
loglikcubecsi
, inibestcubecsi
, CUBE
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