R Under development (unstable) (2024-09-03 r87093 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(tramvs) Loading required package: tram Loading required package: mlt Loading required package: basefun Loading required package: variables Loading required package: mvtnorm > > test_check("tramvs") L0-penalized tram: Normal Linear Regression Model Call: modFUN(formula = formula, data = data, fixed = fix0, theta = theta_init[!names(theta_init) %in% I0]) Coefficients: x.1 x.2 x.3 x.4 x.5 3.265505 3.140816 3.201440 0.000000 0.000000 Log-Likelihood: -134.755 (df = 5) SIC: 142.1287 Active set: x.1 x.2 x.3 L0-penalized tram: Normal Linear Regression Model Call: modFUN(formula = formula, data = data, fixed = fix0, theta = theta_init[!names(theta_init) %in% I0]) Coefficients: x.1 x.2 x.3 x.4 x.5 3.265505 3.140816 3.201440 0.000000 0.000000 Log-Likelihood: -134.755 (df = 5) SIC: supp SIC 1 1 288.6007 2 2 258.6662 3 3 142.1287 4 4 144.5393 5 5 146.9685 Active set: x.1 x.2 x.3 Loading required package: abess Thank you for using abess! To acknowledge our work, please cite the package: Zhu J, Wang X, Hu L, Huang J, Jiang K, Zhang Y, Lin S, Zhu J (2022). 'abess: A Fast Best Subset Selection Library in Python and R.' Journal of Machine Learning Research, 23(202), 1-7. https://www.jmlr.org/papers/v23/21-1060.html. Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Model was fitted to log(y + 1). Loading required package: ordinal Loading required package: TH.data Loading required package: survival Loading required package: MASS Attaching package: 'TH.data' The following object is masked from 'package:MASS': geyser [ FAIL 0 | WARN 0 | SKIP 0 | PASS 9 ] > > proc.time() user system elapsed 134.45 13.93 148.82