glmmlassoControl: Options for the glmmlasso algorithm
In glmmixedlasso: Generalized Linear Mixed Models with Lasso

Description Usage Arguments Details References

View source: R/glmmlassoControl.R

Definition of various kinds of options in the algorithm.

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 fixed-effects 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 cross-validation 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 fixed-effects 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