Description Usage Arguments Details References
View source: R/glmmlassoControl.R
Definition of various kinds of options in the algorithm.
1 2 3 4 5 6 7  glmmlassoControl(family, verbose = 0, maxIter = 200, number = 0,
CovOpt=c("nlminb"), fctSave = TRUE, a_init = 1, delta = 0.5,
rho = 0.1,gamm = 0, lower = 10^(6),
upper = ifelse(family == "binomial", 10^5,10^3), seed = 418,
maxArmijo = 20, min.armijo = TRUE, thres = 10^(4),
tol1 = 10^(6), tol2 = 10^(6), tol3 = 10^(3), tol4 = 10^(8),
gradTol = 10^(3))

family 
a GLM family. Currently implemented are "binomial" (default) and "poisson". 
verbose 
integer. 0 prints no output, 1 prints the outer iteration step, 2 prints the current function value, 3 prints the values of the convergence criteria 
maxIter 
maximum number of (outer) iterations 
number 
integer. Determines the active set algorithm. The zero fixedeffects coefficients are only updated each number iteration. Use 0 ≤ number ≤ 10. 
CovOpt 
character string indicating which covariance parameter optimizer to use. Currently, only "nlminb" is implemented 
fctSave 
Should all evaluation of the objective function be stored? It may help to identify the convergence pattern of the algorithm. 
a_init 
α_{init} in the Armijo step. 
delta 
δ in the Armijo step. 
rho 
ρ in the Armijo step. 
gamm 
γ in the Armijo step. 
lower 
lower bound for the Hessian 
upper 
upper bound for the Hessian 
seed 
set.seed in order to choose the same starting value in the crossvalidation for the fixed effects 
maxArmijo 
maximum number of steps to be chosen in the Armijo step. If the maximum is reached, the algorithm continues with optimizing the next coordinate. 
min.armijo 
logical. If TRUE, the smallest l in the Armijo step is increased, as suggested in Tseng and Yun (2009). Otherwise l always starts with 0. 
thres 
if a variance or covariance parameter has smaller absolute value than thres, the parameter is set to exactly zero, 
tol1 
convergence tolerance for the relative change in the function value 
tol2 
convergence tolerance for the relative change in the fixedeffects parameters 
tol3 
convergence tolerance for the relative change in the covariance parameters 
tol4 
convergence tolerance in the PIRLS algorithm 
gradTol 
the tolerance for the gradient accepted without giving a warning 
For the Armijo step parameters, see Bertsekas (2003).
Dimitri P. Bertsekas (2003) Nonlinear Programming, Athena Scientific.
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