bGLMMControl {GMMBoost}

Control Values for bGLMM 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

bGLMMControl(nue=0.1, lin="(Intercept)", start=NULL, q_start=NULL, OPT=TRUE,  
             sel.method="aic", steps=500, method="EM",
             overdispersion=FALSE,print.iter=TRUE)

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.

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 500.

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.

overdispersion

logical scalar. If FALSE, no scale parameter is derived, if TRUE, in each boosting iteration a scale parameter is estimated by use of Pearson residuals. This can be used to fit overdispersed Poisson models. Default is FALSE.

print.iter

logical. Should the number of interations be printed?. Default is TRUE.

Value

a list with components for each of the possible arguments.

Author(s)

Andreas Groll [e-mail]

See Also

bGLMM, bobyqa

Examples

# decrease the maximum number of boosting iterations 
# and use BIC for selection
bGLMMControl(steps = 100, sel.method = "BIC")