Description Usage Arguments Value Author(s) Examples
With the exception of verbose
it is not recommended to change any of the default values.
1 2 3 4 5 6 7 8 9 10 11 12 | lsgl.algorithm.config(tolerance_penalized_main_equation_loop = 1e-10,
tolerance_penalized_inner_loop_alpha = 1e-04,
tolerance_penalized_inner_loop_beta = 1,
tolerance_penalized_middel_loop_alpha = 0.01,
tolerance_penalized_outer_loop_alpha = 0.01,
tolerance_penalized_outer_loop_beta = 0,
tolerance_penalized_outer_loop_gamma = 1e-05,
use_bound_optimization = FALSE,
use_stepsize_optimization_in_penalizeed_loop = TRUE,
stepsize_opt_penalized_initial_t = 1, stepsize_opt_penalized_a = 0.1,
stepsize_opt_penalized_b = 0.1, max_iterations_outer = 10000,
inner_loop_convergence_limit = 1e+05, verbose = TRUE)
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tolerance_penalized_main_equation_loop |
tolerance threshold. |
tolerance_penalized_inner_loop_alpha |
tolerance threshold. |
tolerance_penalized_inner_loop_beta |
tolerance threshold. |
tolerance_penalized_middel_loop_alpha |
tolerance threshold. |
tolerance_penalized_outer_loop_alpha |
tolerance threshold. |
tolerance_penalized_outer_loop_beta |
tolerance threshold. |
tolerance_penalized_outer_loop_gamma |
tolerance threshold. |
use_bound_optimization |
if |
use_stepsize_optimization_in_penalizeed_loop |
if |
stepsize_opt_penalized_initial_t |
initial step-size. |
stepsize_opt_penalized_a |
step-size optimization parameter. |
stepsize_opt_penalized_b |
step-size optimization parameter. |
max_iterations_outer |
max iteration of outer loop |
inner_loop_convergence_limit |
inner loop convergence limit. |
verbose |
If |
A configuration.
Martin Vincent
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | set.seed(100) # This may be removed, it ensures consistency of the daily tests
## Simulate from Y=XB+E, the dimension of Y is N x K, X is N x p, B is p x K
N <- 50 #number of samples
p <- 50 #number of features
K <- 25 #number of groups
B<-matrix(sample(c(rep(1,p*K*0.1),rep(0, p*K-as.integer(p*K*0.1)))),nrow=p,ncol=K)
X<-matrix(rnorm(N*p,1,1),nrow=N,ncol=p)
Y<-X%*%B+matrix(rnorm(N*K,0,1),N,K)
# Create configuration
config <- lsgl.algorithm.config(verbose = FALSE)
lambda<-lsgl::lambda(X,Y, alpha=1, lambda.min=.5, intercept=FALSE, algorithm.config = config)
fit <-lsgl::fit(X,Y, alpha=1, lambda = lambda, intercept=FALSE, algorithm.config = config)
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