pena_est_fda_scale: Estimate a partially functional linear regression model with...

In wangwustat/fdagroup: Latent Group Detection in Partially Functional Linear Regression Models

Description

The estimator is a variant of the penalization estimator implemented with a ADMM algorithm. The number of groups and the number of principal components are determined by the BIC criterion.

Usage

pena_est_fda_scale(data_list, num_pca_vec, penalty = "SCAD",
  est_fix_eff = TRUE, precision = c(1e-05, 1e-05),
  lam_max_interval = c(0.1, 10), lam_len = 100, lamGroup_a = NULL,
  scale_pena = FALSE)

Arguments

data_list	A list of data. Several elements must be present in the list. The reponse y, the functional covariate x_recv, the scalar covariates z, and an index matrix index. The functional covariate x_recv must be generated from the fda package by, e.g., spline smoothing. The scalar covariates z is a matrix. The index matrix index is a data.frame recording the structure of the data. The first column of index is the family number, the second column is the within family index. The column names of index must be ind_b and ind_w.
penalty	A string indicates which penalty function to use, SCAD or LASSO.
est_fix_eff	A logical value. If TRUE, then the fixed effects are estimated. Otherwise, the fixed effects are not estimated
precision	A vector of thresholds for terminating the ADMM algorithm.
lam_max_interval	A vector indicating the search limits for the maximum of lambda such all the families are clutered into one group.
lam_len	The size of grid for searching lambda.
lamGroup_a	A vecotr of candidate values for lambda. If not provided, the algorithm will determine it automatically.
scale_pena	A logical value, whether to scale the covariates before applying the ADMM algorithm.
num_pca	A vector of candidate number of principal components.

wangwustat/fdagroup documentation built on Dec. 5, 2019, 12:51 a.m.