{"id":17111694,"url":"https://github.com/jbytecode/eive","last_synced_at":"2025-03-23T22:22:15.595Z","repository":{"id":45900769,"uuid":"181943850","full_name":"jbytecode/eive","owner":"jbytecode","description":"An R package for Errors-in-variables estimation in linear regression","archived":false,"fork":false,"pushed_at":"2023-08-21T09:09:33.000Z","size":381,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-29T05:34:10.467Z","etag":null,"topics":["compact-genetic-algorithm","errors-in-variables","linear-regression","r"],"latest_commit_sha":null,"homepage":"","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/jbytecode.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","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,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-04-17T18:05:08.000Z","updated_at":"2022-08-06T13:38:25.000Z","dependencies_parsed_at":"2024-12-01T01:42:23.519Z","dependency_job_id":"9095631f-7f2d-4e73-9993-ff99a40c64b5","html_url":"https://github.com/jbytecode/eive","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jbytecode%2Feive","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jbytecode%2Feive/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jbytecode%2Feive/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jbytecode%2Feive/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jbytecode","download_url":"https://codeload.github.com/jbytecode/eive/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245176404,"owners_count":20572944,"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":["compact-genetic-algorithm","errors-in-variables","linear-regression","r"],"created_at":"2024-10-14T16:57:05.266Z","updated_at":"2025-03-23T22:22:15.562Z","avatar_url":"https://github.com/jbytecode.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# eive\nAn R package for Errors-in-variables estimation in linear regression\n\n## Installation\n\n### Install stable version from CRAN\n\n```R\ninstall.packages(\"eive\")\n```\n\n\n### Install development version \n\nPlease install ```devtools``` package before installing ```eive```:\n\n```R\ninstall.packages(\"devtools\")\n```\n\nthen install the package from the github repo using\n\n```R\ndevtools::install_github(repo = \"https://github.com/jbytecode/eive\") \n```\n\n# The Problem \n\nSuppose the linear regression model is \n\n$$\ny = \\beta_0 + \\beta_1 x^* + \\varepsilon\n$$\n\nwhere $y$ is n-vector of the response variable, $\\beta_0$ and $\\beta_1$ are unknown regression parameteres, $\\varepsilon$ is the iid. error term, $x^*$ is the unknown n-vector of the independent variable, and $n$ is the number of observations.\n\nWe call $x^*$ unknown because in some situations the true values of the variable cannot be visible or directly observable, or observable with some measurement error. Now suppose that $x$ is the observable version of the true values and it is defined as \n\n$$\nx = x^* + \\delta\n$$\n\nwhere $\\delta$ is the measurement error and $x$ is the erroneous version of the true $x^*$. If the estimated model is \n\n$$\n\\hat{y} = \\hat{\\beta_0} + \\hat{\\beta_1}x \n$$\n\nthen the ordinary least squares (OLS) estimates are no longer unbiased and even consistent. \n\nEive-cga is an estimator devised for this problem. The aim is to reduce the errors-in-variable bias with some cost of increasing the variance. At the end, the estimator obtains lower Mean Square Error (MSE) values defined as\n\n$$\nMSE(\\hat{\\beta_1}) = Var(\\hat{\\beta_1}) + Bias^2(\\hat{\\beta_1})\n$$\n\nfor the Eive-cga estimator. For more detailed comparisons, see the original paper given in the Citation part. \n\n# Usage \n\nFor the single variable case \n\n```R \n\u003e eive(dirtyx = dirtyx, y = y, otherx = nothing) \n```\n\nand for the multiple regression \n\n```R \n\u003e eive(dirtyx = dirtyx, y = y, otherx = matrixofotherx) \n```\n\nand for the multiple regression with formula object \n\n```R \n\u003e eive(formula = y ~ x1 + x2 + x3, dirtyx.varname = \"x\", data = mydata) \n```\n\nNote that the method assumes there is only one erroneous variable in the set of independent variables.\n\n### Citation \n```bibtex\n@article{satman2015reducing,\n  title={Reducing errors-in-variables bias in linear regression using compact genetic algorithms},\n  author={Satman, M Hakan and Diyarbakirlioglu, Erkin},\n  journal={Journal of Statistical Computation and Simulation},\n  volume={85},\n  number={16},\n  pages={3216--3235},\n  year={2015},\n  doi={10.1080/00949655.2014.961157}\n  publisher={Taylor \\\u0026 Francis}\n}\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjbytecode%2Feive","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjbytecode%2Feive","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjbytecode%2Feive/lists"}