OrdinalBoostControl {GMMBoost}

Control Values for OrdinalBoost fit

Description

The values supplied in the function call replace the defaults and a list with all possible arguments is returned. The returned list is used as the control argument to the bGLMM function.

Usage

OrdinalBoostControl(nue=0.1, lin=NULL, katvar=NULL, start=NULL, q_start=NULL, 
                    OPT=TRUE, sel.method="aic", steps=100, method="EM", maxIter=500,
                    print.iter.final=FALSE, eps.final=1e-5)

Arguments

nue

weakness of the learner. Choose 0 < nue =< 1. Default is 0.1.

lin

a vector specifying fixed effects, which are excluded from selection.

katvar

a vector specifying category-specific covariates, which are also excluded from selection.

start

a vector containing starting values for fixed and random effects of suitable length. Default is a vector full of zeros.

q_start

a scalar or matrix of suitable dimension, specifying starting values for the random-effects variance-covariance matrix. Default is a scalar 0.1 or diagonal matrix with 0.1 in the diagonal.

OPT

logical scalar. When TRUE the estimates at the optimal number of boosting steps, chosen by information criteria, are derived. If FALSE, the estimates at the maximal number of boosting steps are derived. Default is TRUE.

sel.method

two different information criteria, "aic" or "bic", can be chosen, on which the selection step is based on. Default is "aic".

steps

the number of boosting interations. Default is 100.

method

two methods for the computation of the random-effects variance-covariance parameter estimates can be chosen, an EM-type estimate and an REML-type estimate. The REML-type estimate uses the bobyqa function for optimization. Default is EM.

maxIter

the number of interations for the final Fisher scoring reestimation procedure. Default is 500.

print.iter.final

logical. Should the number of interations in the final re-estimation step be printed?. Default is FALSE.

eps.final

controls the speed of convergence in the final re-estimation. Default is 1e-5.

Value

a list with components for each of the possible arguments.

Author(s)

Andreas Groll [e-mail]

See Also

OrdinalBoost, bobyqa

Examples

# decrease the maximum number of boosting iterations 
# and use BIC for selection
OrdinalBoostControl(steps = 10, sel.method = "BIC")