{"id":32201018,"url":"https://github.com/mwheymans/psfmi","last_synced_at":"2025-10-22T03:58:57.746Z","repository":{"id":46071004,"uuid":"129861191","full_name":"mwheymans/psfmi","owner":"mwheymans","description":"psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets","archived":false,"fork":false,"pushed_at":"2023-06-17T13:01:28.000Z","size":5194,"stargazers_count":11,"open_issues_count":5,"forks_count":7,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-22T03:58:54.111Z","etag":null,"topics":["cox-regression","imputation","imputed-datasets","logistic","multiple-imputation","pool","predictor","regression","selection","spline","spline-predictors"],"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/mwheymans.png","metadata":{"files":{"readme":"README.Rmd","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}},"created_at":"2018-04-17T07:05:18.000Z","updated_at":"2025-05-06T05:17:19.000Z","dependencies_parsed_at":"2023-01-23T10:56:35.261Z","dependency_job_id":"6dc0887b-a065-413e-879e-aa5176608d4f","html_url":"https://github.com/mwheymans/psfmi","commit_stats":{"total_commits":406,"total_committers":4,"mean_commits":101.5,"dds":0.270935960591133,"last_synced_commit":"85b84a95b06c20a53537bdff050bbafb474b30d6"},"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/mwheymans/psfmi","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwheymans%2Fpsfmi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwheymans%2Fpsfmi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwheymans%2Fpsfmi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwheymans%2Fpsfmi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mwheymans","download_url":"https://codeload.github.com/mwheymans/psfmi/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mwheymans%2Fpsfmi/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280376547,"owners_count":26320275,"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","status":"online","status_checked_at":"2025-10-22T02:00:06.515Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["cox-regression","imputation","imputed-datasets","logistic","multiple-imputation","pool","predictor","regression","selection","spline","spline-predictors"],"created_at":"2025-10-22T03:58:56.857Z","updated_at":"2025-10-22T03:58:57.740Z","avatar_url":"https://github.com/mwheymans.png","language":"R","readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# psfmi\n\n[![CRAN_Release_Badge](https://www.r-pkg.org/badges/version-ago/psfmi)](https://CRAN.R-project.org/package=psfmi)\n[![Monthly downloads badge](https://cranlogs.r-pkg.org/badges/last-month/psfmi?color=blue)](https://CRAN.R-project.org/package=psfmi)\n[![R-CMD-check](https://github.com/mwheymans/psfmi/workflows/R-CMD-check/badge.svg)](https://github.com/mwheymans/psfmi/actions)\n[![minimal R version](https://img.shields.io/badge/R%3E%3D-4.0.0-6666ff.svg)](https://cran.r-project.org/)\n\nThe package provides functions to apply pooling, backward and forward selection \nof linear, logistic and Cox regression models across multiply imputed data sets \nusing Rubin's Rules (RR). The D1, D2, D3, D4 and the median p-values method can be\nused to pool the significance of categorical variables (multiparameter test). \nThe model can contain\tcontinuous, dichotomous, categorical and restricted cubic \nspline predictors and interaction terms between all these type of variables. \nVariables can also be forced in the model during selection. \n\nValidation of the prediction models can be performed with cross-validation or \nbootstrapping across multiply imputed data sets and pooled model performance measures \nas AUC value, Reclassification, R-square, Hosmer and Lemeshow test, scaled Brier score and calibration \nplots are generated. Also a function to externally validate logistic\tprediction models \nacross multiple imputed data sets is available and a function to compare models \nin multiply imputed data.\n\n## Installation\n\nYou can install the released version of psfmi with:\n\n``` r\ninstall.packages(\"psfmi\")\n```\nAnd the development version from [GitHub](https://github.com/) with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"mwheymans/psfmi\")\n```\n## Citation\n\nCite the package as:\n\n``` r\n\nMartijn W Heymans (2021). psfmi: Prediction Model Pooling, Selection and Performance Evaluation \nAcross Multiply Imputed Datasets. R package version 1.1.0. https://mwheymans.github.io/psfmi/ \n\n```\n## Examples\n\nThis example shows you how to pool a logistic regression model across 5 multiply imputed \ndatasets and that includes two restricted cubic spline variables and a categorical, continuous\nand dichotomous variable. The pooling method that is used is method D1.\n\n```{r }\nlibrary(psfmi)\n\npool_lr \u003c- psfmi_lr(data=lbpmilr, formula = Chronic ~ rcs(Pain, 3) + \n                      JobDemands + rcs(Tampascale, 3) + factor(Satisfaction) + \n                      Smoking, nimp=5, impvar=\"Impnr\", method=\"D1\")\n\npool_lr$RR_model\n\npool_lr$multiparm\n```\n\nThis example shows you how to apply forward selection of the above model using a p-value of 0.05. \n\n```{r }\nlibrary(psfmi)\n\npool_lr \u003c- psfmi_lr(data=lbpmilr, formula = Chronic ~ rcs(Pain, 3) + \n                      JobDemands + rcs(Tampascale, 3) + factor(Satisfaction) + \n                      Smoking, p.crit = 0.05, direction=\"FW\", \n                      nimp=5, impvar=\"Impnr\", method=\"D1\")\n\npool_lr$RR_model_final\n\npool_lr$multiparm\n```\n\nMore examples for logistic, linear and Cox regression models as well as internal and external validation of prediction models can be found on the [package website](https://mwheymans.github.io/psfmi/) or in the online book [Applied Missing Data Analysis](https://bookdown.org/mwheymans/bookmi/). \n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmwheymans%2Fpsfmi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmwheymans%2Fpsfmi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmwheymans%2Fpsfmi/lists"}