glmmlassoControl {glmmixedlasso} | R Documentation |
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 |
For the Armijo step parameters, see Bertsekas (2003).
Dimitri P. Bertsekas (2003) Nonlinear Programming, Athena Scientific.