{"Package":"partR2","Title":"Partitioning R2 in GLMMs","Version":"0.9.2","Depends":"R (>= 3.5.0)","Authors@R":"c(person(given = \"Martin A.\",\nfamily = \"Stoffel\",\nrole = c(\"aut\", \"cre\"),\nemail = \"martin.adam.stoffel@gmail.com\"),\nperson(given = \"Shinichi\",\nfamily = \"Nakagawa\",\nrole = c(\"aut\"),\nemail = \"s.nakagawa@unsw.edu.au\"),\nperson(given = \"Holger\",\nfamily = \"Schielzeth\",\nrole = c(\"aut\"),\nemail = \"holger.schielzeth@uni-jena.de\"))","Description":"Partitioning the R2 of GLMMs into variation explained by each\npredictor and combination of predictors using semi-partial (part) R2 and\ninclusive R2. Methods are based on the R2 for GLMMs described in\nNakagawa & Schielzeth (2013) and Nakagawa, Johnson & Schielzeth (2017).","License":"GPL (>= 2)","URL":"https://github.com/mastoffel/partR2","BugReports":"https://github.com/mastoffel/partR2/issues","Imports":"methods, stats, lme4 (>= 1.1-21), pbapply (>= 1.4-2), dplyr\n(>= 1.0.0), purrr (>= 0.3.3), rlang (>= 0.4.2), tibble (>=\n2.1.3), magrittr (>= 1.5), ggplot2 (>= 3.3.0), tidyr (>= 1.1)","Encoding":"UTF-8","LazyData":"true","RoxygenNote":"7.2.3","Suggests":"testthat, future, furrr, knitr, rmarkdown, patchwork, covr","VignetteBuilder":"knitr","NeedsCompilation":"no","Packaged":"2024-03-04 13:03:24 UTC; mstoffel","Author":"Martin A. Stoffel [aut, cre],\nShinichi Nakagawa [aut],\nHolger Schielzeth [aut]","Maintainer":"Martin A. Stoffel ","Repository":"CRAN","Date/Publication":"2024-03-04 13:20:02 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/partR2_0.9.2.tar.gz","jsonAuthors":[{"name":"Martin A. Stoffel","email":"martin.adam.stoffel@gmail.com","maintainer":true},{"name":"Shinichi Nakagawa","email":"s.nakagawa@unsw.edu.au","maintainer":false},{"name":"Holger Schielzeth","email":"holger.schielzeth@uni-jena.de","maintainer":false}],"readme":"\n\n\n[![CRAN total\ndownloads](http://cranlogs.r-pkg.org/badges/grand-total/partR2?color=blue)](https://cran.r-project.org/package=partR2)\n[![Monthly downloads\nbadge](https://cranlogs.r-pkg.org/badges/last-month/partR2?color=blue)](https://CRAN.R-project.org/package=partR2)\n[![Lifecycle:\nmaturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html#maturing)\n[![Codecov test\ncoverage](https://codecov.io/gh/mastoffel/partR2/branch/master/graph/badge.svg)](https://app.codecov.io/gh/mastoffel/partR2?branch=master)\n\n\n\n\n# partR2\n\nThe goal of `partR2` is to estimate R² in GLMMs (sensu\nNakagawa & Schielzeth 2013) and to partition the R² into the\nvariance explained by the predictors.\n\nThe package takes a fitted lme4 model as input and gives you:\n\n- R² (marginal or conditional)\n- Part (semi-partial) R², the variance explained uniquely by\n each predictor and combinations of predictors\n- Inclusive R², the variance explained by a predictor\n independent of all other predictors\n- Structure coefficients, the correlation between a predictor and the\n fitted response\n- Beta weights, standardised model estimates\n\nAll estimates can be combined with parametric bootstrapping to get\nconfidence intervals.\n\n## Installation\n\nYou can install the stable version of `partR2` from CRAN with:\n\n``` r\ninstall.packages(\"partR2\")\n```\n\nOr the development version from GitHub with:\n\n``` r\n# install.packages(\"remotes\")\nremotes::install_github(\"mastoffel/partR2\", build_vignettes = TRUE, dependencies = TRUE) \n```\n\nAccess the vignette with:\n\n``` r\n# check vignette\nbrowseVignettes(\"partR2\")\n```\n\n`partR2` is still in an early phase of development and might contain\nbugs. If you find one, please report a minimal reproducible example in\nthe [issues](https://github.com/mastoffel/partR2/issues).\n\n### Citation\n\nWhen using `partR2`, please cite our\n[paper](https://peerj.com/articles/11414/):\n\nStoffel MA, Nakagawa S, Schielzeth H. 2021. partR2: partitioning R2 in\ngeneralized linear mixed models. *PeerJ* **9**:e11414\n\n\n## Example\n\n``` r\nlibrary(partR2)\nlibrary(lme4)\n\n?`partR2-package`\n\n# load data\ndata(biomass)\n# fit lme4 model\nmod <- lmer(Biomass ~ Year + Temperature + SpeciesDiversity + (1|Population),\n data = biomass)\n# R2s and partial R2s\n(R2 <- partR2(mod, partvars = c(\"SpeciesDiversity\", \"Temperature\", \"Year\"),\n R2_type = \"marginal\", nboot = 100, CI = 0.95))\n#> \n#> \n#> R2 (marginal) and 95% CI for the full model: \n#> R2 CI_lower CI_upper nboot ndf\n#> 0.5133 0.4439 0.5908 100 4 \n#> \n#> ----------\n#> \n#> Part (semi-partial) R2:\n#> Predictor(s) R2 CI_lower CI_upper nboot ndf\n#> Model 0.5133 0.4439 0.5908 100 4 \n#> SpeciesDiversity 0.1729 0.0736 0.2913 100 3 \n#> Temperature 0.3058 0.2193 0.4106 100 3 \n#> Year 0.0140 0.0000 0.1537 100 3 \n#> SpeciesDiversity+Temperature 0.4916 0.4205 0.5714 100 2 \n#> SpeciesDiversity+Year 0.1862 0.0883 0.3029 100 2 \n#> Temperature+Year 0.3276 0.2430 0.4293 100 2 \n#> SpeciesDiversity+Temperature+Year 0.5133 0.4439 0.5908 100 1\n```\n\nAnd to plot the results:\n\n``` r\nforestplot(R2, type = \"R2\", line_size = 0.7, text_size = 14, point_size = 3)\n```\n\n![](README-plot-1.png)","jobInfo":{"package":"partR2","version":"0.9.2","parsingStatus":"success","parserVersion":2,"parsedAt":"2024-03-04T14:30:17+0000"}}

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