{"id":16735104,"url":"https://github.com/mamba413/rvs","last_synced_at":"2025-04-10T12:15:50.125Z","repository":{"id":103329357,"uuid":"342160035","full_name":"Mamba413/rvs","owner":"Mamba413","description":"Robust variable selection with exponential squared loss","archived":false,"fork":false,"pushed_at":"2024-01-11T14:19:18.000Z","size":136,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-06T21:49:44.932Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Mamba413.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-02-25T07:32:04.000Z","updated_at":"2025-02-16T12:47:16.000Z","dependencies_parsed_at":"2024-01-11T16:17:18.305Z","dependency_job_id":"18ae9929-9ba0-445f-8330-f1c4957e79a5","html_url":"https://github.com/Mamba413/rvs","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Frvs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Frvs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Frvs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Frvs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mamba413","download_url":"https://codeload.github.com/Mamba413/rvs/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248217084,"owners_count":21066633,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-13T00:04:57.675Z","updated_at":"2025-04-10T12:15:50.091Z","avatar_url":"https://github.com/Mamba413.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n# robustlm\n\nThe goal of robustlm is to carry out robust variable selection through\nexponential squared loss. Specifically, it solves the following\noptimization problem:\n\n\\[\n\\arg\\min_{\\beta} \\sum_{i=1}^n(1-\\exp\\{-(y_i-x_i^T\\beta)^2/\\gamma_n\\})+n\\sum_{i=1}^d \\lambda_{n j}|\\beta_{j}|.\n\\]\n\nWe use the adaptive LASSO penalty. Regularization parameters are chosen\nadaptively by default, while they can be supplied by the user. Block\ncoordinate gradient descent algorithm is used to efficiently solve the\noptimization problem.\n\n\u003c!-- ## Installation --\u003e\n\n\u003c!-- You can install the released version of robustlm from [CRAN](https://CRAN.R-project.org) with: --\u003e\n\n\u003c!-- ``` r --\u003e\n\n\u003c!-- install.packages(\"robustlm\") --\u003e\n\n\u003c!-- ``` --\u003e\n\n## Example\n\nThis is a basic example which shows you how to use this package. First, we generate data which contain influential points in the response: \n\n```\nset.seed(1)\nlibrary(MASS)\nN \u003c- 1000\np \u003c- 8\nrho \u003c- 0.5\nbeta_true \u003c- c(1, 1.5, 2, 1, 0, 0, 0, 0)\nH \u003c- abs(outer(1:p, 1:p, \"-\"))\nV \u003c- rho^H\nX \u003c- mvrnorm(N, rep(0, p), V)\n\n# generate error term from a mixture normal distribution\ncomponents \u003c- sample(1:2, prob=c(0.8, 0.2), size=N, replace=TRUE)\nmus \u003c- c(0, 10)\nsds \u003c- c(1, 6)\nerr \u003c- rnorm(n=N,mean=mus[components],sd=sds[components])\n\nY \u003c- X %*% beta_true + err\n```\n\nWe apply *robustlm* function to select important variables:\n```\nlibrary(robustlm)\nrobustlm1 \u003c- robustlm(X, Y)\nrobustlm1\n#\u003e $beta\n#\u003e [1] 0.9411568 1.5839011 2.0716890 0.9489619 0.0000000 0.0000000 0.0000000\n#\u003e [8] 0.0000000\n#\u003e \n#\u003e $alpha\n#\u003e [1] 0\n#\u003e \n#\u003e $gamma\n#\u003e [1] 8.3\n#\u003e \n#\u003e $weight\n#\u003e [1]   87.140346    7.033846    4.340160    3.343782    6.833033  703.863830\n#\u003e [7]  193.860493  858.412613 2183.876884\n#\u003e \n#\u003e $loss\n#\u003e [1] 250.3821\n```\nThe estimated regression coefficients $(0.94, 1.58, 2.07, 0.95, 0.00, 0.00, 0.00, 0.00)$ are close to the true values$(1, 1.5, 2, 1, 0, 0, 0, 0)$. There is no mistaken selection or discard. ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Frvs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmamba413%2Frvs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Frvs/lists"}