bGLMMControl: 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)

Value

a list with components for each of the possible arguments.

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.

Author

Andreas Groll andreas.groll@stat.uni-muenchen.de

Examples

Run this code

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

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