Set control parameters for cumulative link models
clm.control(method = c("Newton", "model.frame", "design", "ucminf", "nlminb",
"optim"),
sign.location = c("negative", "positive"),
sign.nominal = c("positive", "negative"),
..., trace = 0L,
maxIter = 100L, gradTol = 1e-06, maxLineIter = 15L, relTol = 1e-6,
tol = sqrt(.Machine$double.eps), maxModIter = 5L,
convergence = c("warn", "silent", "stop", "message"))a list of control parameters.
"Newton" fits the model by maximum likelihood and
"model.frame" cause clm to return the
model.frame, "design" causes clm to
return a list of design matrices etc. that can be used with
clm.fit. ucminf, nlminb and optim refer
to general purpose optimizers.
change sign of the location part of the model.
change sign of the nominal part of the model.
numerical, if > 0 information is printed about and during
the optimization process. Defaults to 0.
the maximum number of Newton-Raphson iterations.
Defaults to 100.
the maximum absolute gradient; defaults to 1e-6.
the maximum number of step halfings allowed if
a Newton(-Raphson) step over shoots. Defaults to 15.
relative convergence tolerence: relative change in the
parameter estimates between Newton iterations. Defaults to 1e-6.
numerical tolerence on eigenvalues to determine negative-definiteness of Hessian. If the Hessian of a model fit is negative definite, the fitting algorithm did not converge. If the Hessian is singular, the fitting algorithm did converge albeit not to a unique optimum, so one or more parameters are not uniquely determined even though the log-likelihood value is.
the maximum allowable number of consecutive
iterations where the Newton step needs to be modified to be a decent
direction. Defaults to 5.
action to take if the fitting algorithm did not converge.
Rune Haubo B Christensen
clm