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In glmmixedlasso: Generalized Linear Mixed Models with Lasso
Description Usage Arguments Details References View source: R/glmmlassoControl.R DescriptionDefinition of various kinds of options in the algorithm. Usage 1 2 3 4 5 6 7glmmlassoControl(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 familya GLM family. Currently implemented are "binomial" (default) and "poisson". verboseinteger. 0 prints no output, 1 prints the outer iteration step, 2 prints the current function value, 3 prints the values of the convergence criteria maxItermaximum number of (outer) iterations numberinteger. Determines the active set algorithm. The zero fixed-effects coefficients are only updated each number iteration. Use 0 ≤ number ≤ 10. CovOptcharacter string indicating which covariance parameter optimizer to use. Currently, only "nlminb" is implemented fctSaveShould all evaluation of the objective function be stored? It may help to identify the convergence pattern of the algorithm. a_initα_{init} in the Armijo step. deltaδ in the Armijo step. rhoρ in the Armijo step. gammγ in the Armijo step. lowerlower bound for the Hessian upperupper bound for the Hessian seedset.seed in order to choose the same starting value in the cross-validation for the fixed effects maxArmijomaximum number of steps to be chosen in the Armijo step. If the maximum is reached, the algorithm continues with optimizing the next coordinate. min.armijological. If TRUE, the smallest l in the Armijo step is increased, as suggested in Tseng and Yun (2009). Otherwise l always starts with 0. thresif a variance or covariance parameter has smaller absolute value than thres, the parameter is set to exactly zero, tol1convergence tolerance for the relative change in the function value tol2convergence tolerance for the relative change in the fixed-effects parameters tol3convergence tolerance for the relative change in the covariance parameters tol4convergence tolerance in the PIRLS algorithm gradTolthe tolerance for the gradient accepted without giving a warning DetailsFor the Armijo step parameters, see Bertsekas (2003). ReferencesDimitri P. Bertsekas (2003) Nonlinear Programming, Athena Scientific. glmmixedlasso documentation built on May 2, 2019, 5:26 p.m. Related to glmmlassoControl in glmmixedlasso... glmmixedlasso index |
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