## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----eval=FALSE--------------------------------------------------------------- # # library(TransGraph) # # load example data from github repository # # Please refer to https://github.com/Ren-Mingyang/example_data_TransGraph for detailed data information # load(url("https://github.com/Ren-Mingyang/example_data_TransGraph/raw/main/example.data.GGM.RData")) # t.data = example.data.GGM$target.list$t.data # t.precision = example.data.GGM$target.list$t.precision # A.data = example.data.GGM$A.data # A.data.infor = example.data.GGM$A.data.infor # # # using all auxiliary domains # res.trans.weight = trans_precision(t.data, A.data, cov.method="weight") # res.trans.opt = trans_precision(t.data, A.data, cov.method="opt") # res.trans.size = trans_precision(t.data, A.data, cov.method="size") # Theta.trans.weight = res.trans.weight$Theta.hat # Theta.trans.opt = res.trans.opt$Theta.hat # Theta.trans.size = res.trans.size$Theta.hat # Theta.single = res.trans.weight$Theta.hat0 # initial rough estimation via the target domain # Theta.single[abs(Theta.single)<0.0001] = 0 # # Evaluation.GGM(Theta.single, t.precision) # Evaluation.GGM(Theta.trans.weight, t.precision) # Evaluation.GGM(Theta.trans.opt, t.precision) # Evaluation.GGM(Theta.trans.size, t.precision) # # # using informative auxiliary domains # res.trans.size.oracle = trans_precision(t.data, A.data.infor, precision.method="CLIME", cov.method="size") # Evaluation.GGM(res.trans.size.oracle$Theta.hat, t.precision) # ## ----eval=FALSE--------------------------------------------------------------- # library(TransGraph) # library(Tlasso) # # load example data from github repository # # Please refer to https://github.com/Ren-Mingyang/example_data_TransGraph for detailed data information # load(url("https://github.com/Ren-Mingyang/example_data_TransGraph/raw/main/example.data.tensorGGM.RData")) # t.data = example.data$t.data # A.data = example.data$A.data # t.Omega.true.list = example.data$t.Omega.true.list # normalize = T # # K = length(A.data) # p.vec = dim(t.data) # M = length(p.vec) - 1 # n = p.vec[M+1] # p.vec = p.vec[1:M] # tla.lambda = 20*sqrt( p.vec*log(p.vec) / ( n * prod(p.vec) )) # A.lambda = list() # for (k in 1:K) { # A.lambda[[k]] = 20*sqrt( log(p.vec) / ( dim(A.data[[k]])[M+1] * prod(p.vec) )) # } # # # the proposed method # res.final = tensor.GGM.trans(t.data, A.data, A.lambda, normalize = normalize) # # Tlasso # Tlasso.Omega.list = Tlasso.fit(t.data, lambda.vec = tla.lambda, norm.type = 1+as.numeric(normalize)) # # # summary # i.Omega = as.data.frame(t(unlist(est.analysis(res.final$Omega.list, t.Omega.true.list)))) # i.Omega.diff = as.data.frame(t(unlist(est.analysis(res.final$Omega.list.diff, t.Omega.true.list)))) # i.Tlasso = as.data.frame(t(unlist(est.analysis(Tlasso.Omega.list, t.Omega.true.list)))) # i.Omega.diff # proposed.v # i.Omega # proposed # i.Tlasso # Tlasso # ## ----eval=FALSE--------------------------------------------------------------- # # library(TransGraph) # # load example data from github repository # # Please refer to https://github.com/Ren-Mingyang/example_data_TransGraph for detailed data information # load(url("https://github.com/Ren-Mingyang/example_data_TransGraph/raw/main/example.data.DAG.RData")) # t.data = example.data.DAG$target.DAG.data$X # true_adjace = example.data.DAG$target.DAG.data$true_adjace # A.data = example.data.DAG$auxiliary.DAG.data$X.list.A # # # transfer method # res.trans = trans.local.DAG(t.data, A.data) # # Topological Layer method-based single-task learning (JLMR, 2022) # res.single = TLLiNGAM(t.data) # # Evaluation.DAG(res.trans$B, true_adjace)$Eval_result # Evaluation.DAG(res.single$B, true_adjace)$Eval_result # ## ----eval=FALSE--------------------------------------------------------------- # # library(TransGraph) # # load example data from github repository # # Please refer to https://github.com/Ren-Mingyang/example_data_TransGraph for detailed data information # load(url("https://github.com/Ren-Mingyang/example_data_TransGraph/raw/main/example.data.singleDAG.RData")) # true_adjace = example.data.singleDAG$true_adjace # t.data = example.data.singleDAG$X # res.single = TLLiNGAM(t.data) # Evaluation.DAG(res.single$B, true_adjace)$Eval_result #