AFSSEN_CV | R Documentation |
For a simulation data, with computing the optimum penalty parameters, It computes important variables and produce smooth estimates of their parameters in a function-on-scalar linear model with sub-Gaussian errors and high-dimensional predictors.
AFSSEN_CV(type_kernel = "exponential", param_kernel = 8, thres, number_non_zeros, ratio_lambda_H, number_lambda_H, num_lambda_H_NONad, lambda_H, lambda_K, early_CV, early_CV_thres, max_ite_nadp, max_ite_adp, max_ite_final, target_inc, proportion_training_set = 0.75, verbose = FALSE, fold_ad = 10, N = 500, I = 1000, I0 = 10, nu_beta = 2.5, range = 1/4, variance = 1, nu_eps = 1.5, range_eps = 1/4)
type_kernel |
string. three possible choices are implemented. |
param_kernel |
scalar. Value of the characteristic smoothing parameter of the kernel.
It is the σ parameter of the Gaussian and the Exponential kernel, as introduced
the σ parameter of the Sobolev.
Defualt is |
thres |
scalar. Stopping criteria: beta increment threshold || β^{T} - β^(T-1) ||_{H} < thres Defualt is |
number_non_zeros |
scalar. Stopping Criteria: Kill switch; number of nonzero predictors
Defualt is |
ratio_lambda_H |
scalar. λ_{Hmax}/λ_{Hmin}
Defualt is |
number_lambda_H |
scalar. Generate the number of log-equally spaced λ_{H} in [λ_{Hmin},λ_{Hmax}].
Defualt is |
num_lambda_H_NONad |
scalar. Number of λ_H in non-adaptive step
Defualt is |
lambda_H |
vector. You have option to insert directly a vector of λ_H.
Defualt is |
lambda_K |
vector. Vector of λ_{K}. |
early_CV |
binary. 0 or 1 : applying the |
early_CV_thres |
scalar. Stopping Criteria: Breaking point in CV plot. |CV(h-1,k)-CV(h,k)| / |CV(h-1,k)| < early_CV_thres Defualt is |
max_ite_nadp |
scalar. Stopping Criteria: Maximum iteration of coordinate descent algorithm in non-adaptive step
Defualt is |
max_ite_adp |
scalar. Stopping Criteria: Maximum iteration of coordinate descent algorithm in adaptive step
Defualt is |
max_ite_final |
scalar. Stopping Criteria: Maximum iteration of coordinate descent algorithm in final step
Defualt is |
target_inc |
binary. Stopping Criteria: 0 or 1 : if target function is increased, stop
Defualt is |
proportion_training_set |
scalar. value in (0,1), the
proportion for the training set for the Cross Validation in non-adaptive step
Defualt is |
fold_ad |
scalar. Number of fold for using CV in adaptive steps to find optimum λ_{H} and λ_{K} and then the coefficients estimation.
Defualt is |
list containing:
beta :
matrix. final estimation of coefficients.
beta_no_adaptive :
matrix. estimation of coefficients after non-adaptive step.
predictors :
vector. final significant predictors.
predictors_no_adaptive :
vector. significant predictors after non-adaptive step.
lambda_H_opt :
scalar. optimum λ_{H}
lambda_K_opt :
scalar. optimum λ_{K}
pred_error :
scalar. prediction error
pred_error_der :
scalar. prediction error derivative.
## Not run: data(simulation) data(SobolevKernel) time <- proc.time() FLAME_estimation <- FLAME() duration <- proc.time()-time duration names(FLAME_estimation) ## End(Not run)
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