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

## Description

Definition of various kinds of options in the algorithm.

## Usage

 ```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)) ```

## Arguments

 `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

## Details

For the Armijo step parameters, see Bertsekas (2003).

## References

Dimitri P. Bertsekas (2003) Nonlinear Programming, Athena Scientific.

glmmixedlasso documentation built on May 31, 2017, 3:34 a.m.