grouplasso_gt_grid: Fit grouplasso logistic regression estimator with group...

In gregorkb/semipadd2pop: Combine two data sets to fit semiparametric additive models

Fit grouplasso logistic regression estimator with group testing data over a grid of lambda values

Description

Fit grouplasso logistic regression estimator with group testing data over a grid of lambda values

Usage

grouplasso_gt_grid(
  Y,
  Z,
  Se,
  Sp,
  X,
  E.approx = FALSE,
  groups,
  n.lambda,
  lambda.min.ratio,
  lambda.max.ratio = 1,
  w,
  tol = 1e-04,
  maxiter = 500,
  report.prog = FALSE
)

Arguments

Y	Group testing output in the format as output by one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Z	Group testing output in the format as output by one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Se	A vector of testing sensitivities, where the first element is the testing specificity for pools and the second entry is the test specificity for individual testing, if applicable.
Sp	A vector of testing specificities, where the first element is the testing specificity for pools and the second entry is the test specificity for individual testing, if applicable.
X	the matrix with the observed covariate values (including a column of ones for the intercept)
E.approx	a logical indicating whether the conditional expectations in the E-step should be computed approximately or exactly.
groups	a vector indicating to which group each covariate belongs
n.lambda	the number of lambda values desired
lambda.min.ratio	ratio of the smallest lambda value to the smallest value of lambda which admits no variables to the model
lambda.max.ratio	ratio of the largest lambda value to the smallest value of lambda which admits no variables to the model
w	group-specific weights for different penalization of different groups
tol	a convergence criterion
maxiter	the maximum allowed number of iterations (EM steps)
report.prog	a logical. If TRUE then the number of inner loops required to complete the M step of the EM algorithm are returned after each EM step.

Value

a list containing the fits over a grid of lambda values as well as the vector of lambda values

Examples

grouplasso_gt_data <- get_grouplasso_data(n = 1000, response = "gt")

grouplasso_gt_grid.out <- grouplasso_gt_grid(Y = grouplasso_gt_data$Y$I,
                                             Z = grouplasso_gt_data$Y$A,
                                             Se = grouplasso_gt_data$Y$Se,
                                             Sp = grouplasso_gt_data$Y$Sp,
                                             X = grouplasso_gt_data$X,
                                             E.approx = grouplasso_gt_data$Y$E.approx,
                                             groups = grouplasso_gt_data$groups,
                                             n.lambda = 10,
                                             lambda.min.ratio = 0.01,
                                             lambda.max.ratio = 0.50,
                                             w  = grouplasso_gt_data$w,
                                             tol = 1e-3,
                                             maxiter = 500,
                                             report.prog = TRUE)

gregorkb/semipadd2pop documentation built on Oct. 2, 2022, 1:37 p.m.