grouplasso2pop_gt: Compute group lasso for two populations with group testing...

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

Compute group lasso for two populations with group testing data

Description

Compute group lasso for two populations with group testing data

Usage

grouplasso2pop_gt(
  Y1,
  Z1,
  Se1,
  Sp1,
  X1,
  groups1,
  E.approx1 = FALSE,
  Y2,
  Z2,
  Se2,
  Sp2,
  X2,
  groups2,
  E.approx2 = FALSE,
  rho1,
  rho2,
  lambda,
  eta,
  w1,
  w2,
  w,
  AA1,
  AA2,
  Com,
  tol = 0.001,
  maxiter = 1000,
  init = NULL,
  report.prog = TRUE
)

Arguments

Y1	Group testing output for data set 1 in the format as output by one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Z1	Group testing output for data set 1 in the format as output by one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Se1	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.
Sp1	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.
X1	the matrix with the observed covariate values for data set 1 (including a column of ones for the intercept)
groups1	a vector indicating to which group each covariate of data set 2 belongs
E.approx1	a logical indicating whether the conditional expectations in the E-step should be computed approximately or exactly for the first data set
Y2	Group testing output for data set 2 in the format as output by one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Z2	Group testing output for data set 2 in the format as output by one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Se2	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.
Sp2	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.
X2	the matrix with the observed covariate values for data set 2 (including a column of ones for the intercept)
groups2	a vector indicating to which group each covariate of data set 2 belongs
E.approx2	a logical indicating whether the conditional expectations in the E-step should be computed approximately or exactly for the second data set
rho1	weight placed on the first data set
rho2	weight placed on the second data set
lambda	the level of sparsity penalization
eta	the level of penalization towards model similarity
w1	group-specific weights for different penalization across groups in data set 1
w2	group-specific weights for different penalization across groups in data set 2
w	group-specific weights for different penalization toward similarity for different groups
AA1	a list of the matrices A2j
Com	the indices of the covariate groups which are common in the two datasets
tol	a convergence criterion
maxiter	the maximum allowed number of iterations (EM steps)
init	a list of initial values for the coefficient for each of the two data sets
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

Returns the estimator of the semiparametric additive model with group testing data

Examples

grouplasso2pop_gt_data <- get_grouplasso2pop_data( n1 = 1000, n2 = 1200, response = "gt")

grouplassogr2pop.out <- grouplasso2pop_gt(Y1 = grouplasso2pop_gt_data$Y1$I,
                                          Z1 = grouplasso2pop_gt_data$Y1$A,
                                          Se1 = grouplasso2pop_gt_data$Y1$Se,
                                          Sp1 = grouplasso2pop_gt_data$Y1$Sp,
                                          X1 = grouplasso2pop_gt_data$X1,
                                          groups1 = grouplasso2pop_gt_data$groups1,
                                          E.approx1 = grouplasso2pop_gt_data$Y1$E.approx,
                                          Y2 = grouplasso2pop_gt_data$Y2$I,
                                          Z2 = grouplasso2pop_gt_data$Y2$A,
                                          Se2 = grouplasso2pop_gt_data$Y2$Se,
                                          Sp2 = grouplasso2pop_gt_data$Y2$Sp,
                                          X2 = grouplasso2pop_gt_data$X2,
                                          groups2  = grouplasso2pop_gt_data$groups2,
                                          E.approx2 = grouplasso2pop_gt_data$Y2$E.approx,
                                          rho1 = 1,
                                          rho2 = 1,
                                          lambda = 1,
                                          eta = 1,
                                          w1  = grouplasso2pop_gt_data$w1,
                                          w2  = grouplasso2pop_gt_data$w2,
                                          w  = grouplasso2pop_gt_data$w,
                                          AA1  = grouplasso2pop_gt_data$AA1,
                                          AA2  = grouplasso2pop_gt_data$AA2,
                                          Com  = grouplasso2pop_gt_data$Com,
                                          tol = 1e-3,
                                          maxiter = 500,
                                          report.prog = TRUE)

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