Package: partR2 0.9.2

partR2: Partitioning R2 in GLMMs

Partitioning the R2 of GLMMs into variation explained by each predictor and combination of predictors using semi-partial (part) R2 and inclusive R2. Methods are based on the R2 for GLMMs described in Nakagawa & Schielzeth (2013) and Nakagawa, Johnson & Schielzeth (2017).

Authors:Martin A. Stoffel [aut, cre], Shinichi Nakagawa [aut], Holger Schielzeth [aut]

partR2_0.9.2.tar.gz
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partR2.pdf |partR2.html✨
partR2/json (API)
NEWS

# Install 'partR2' in R:
install.packages('partR2', repos = c('https://mastoffel.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mastoffel/partr2/issues

Datasets:

On CRAN:

6.76 score 21 stars 61 scripts 502 downloads 3 mentions 4 exports 43 dependencies

Last updated 2 months agofrom:0b694c4180. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-winNOTENov 23 2024
R-4.5-linuxNOTENov 23 2024
R-4.4-winNOTENov 23 2024
R-4.4-macNOTENov 23 2024
R-4.3-winNOTENov 23 2024
R-4.3-macNOTENov 23 2024

Exports:%>%forestplotmergeR2partR2

Dependencies:bootclicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixmgcvminqamunsellnlmenloptrpbapplypillarpkgconfigpurrrR6RColorBrewerRcppRcppEigenrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Using partR2

Martin A. Stoffel, Shinichi Nakagawa, Holger Schielzeth

Rendered fromUsing_partR2.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2021-01-14
Started: 2020-02-04