Package: int3ract 2.1.0
int3ract: Johnson-Neyman Analysis of Two- and Three-Way Interactions
Reports and plots the conditional effect of each variable involved in a multiplicative interaction across the range of its moderators, together with the region over which that effect is distinguishable from zero. Extends the classic framework of Johnson and Neyman (1936) and Johnson and Fay (1950) <doi:10.1007/BF02288864> to three-way interactions and to Bayesian models. The single entry point JN() dispatches on the fitted object, with methods for lm()/glm() models, 'lme4' models, 'RSiena' and 'multiSiena' results, and matrices of posterior draws; support for further model classes is added by writing one jn_input() method. Results are classed objects with print(), summary() and plot() methods, and the figures carry data-density panels showing how much empirical support each part of the moderator range has. A detailed introduction can be found in Krause (2026) <doi:10.48550/arXiv.2604.22051>.
Authors:
int3ract_2.1.0.tar.gz
int3ract_2.1.0.zip(r-4.7-any)int3ract_2.1.0.zip(r-4.6-any)int3ract_2.1.0.zip(r-4.5-any)
int3ract_2.1.0.tgz(r-4.6-any)int3ract_2.1.0.tgz(r-4.5-any)
int3ract_2.1.0.tar.gz(r-4.7-any)int3ract_2.1.0.tar.gz(r-4.6-any)
int3ract_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
int3ract/json (API)
| # Install 'int3ract' in R: |
| install.packages('int3ract', repos = c('https://rwkrause.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rwkrause/int3ract/issues
Last updated from:4d9c4b99b9. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 179 | ||
| source / vignettes | OK | 229 | ||
| linux-release | OK | 192 | ||
| macos-release | OK | 205 | ||
| macos-oldrel | OK | 184 | ||
| windows-devel | OK | 132 | ||
| windows-release | OK | 131 | ||
| windows-oldrel | OK | 115 | ||
| wasm-release | OK | 135 |
Exports:autoplotJNjn_inputjn_plotsjn_posteriorjn_regionsjn_savejn_stylejn_waldJNK_bayesJNK_freq
Dependencies:cachemclassclassIntclicpp11DBIe1071farverfastmapggpatternggplot2gluegridpatterngtableisobandKernSmoothlabelinglifecyclememoisepatchworkpngproxyR6RColorBrewerRcpprlangs2S7scalessfunitsvctrsviridisLitewithrwk
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract the conditional effects of a Johnson-Neyman analysis | as.data.frame.JN coef.JN vcov.JN |
| Johnson-Neyman analysis of a two- or three-way interaction | JN JN.default JN.jn_input JN.lmerModLmerTest JN.merMod JN.multiSiena JN.sienaBayesFit |
| Carriers for Johnson-Neyman input | jn_input jn_input.data.frame jn_input.default jn_input.lavaan jn_input.lm jn_input.lmerModLmerTest jn_input.matrix jn_input.mcmc jn_input.mcmc.list jn_input.merMod jn_input.multiSiena jn_input.sienaBayesFit jn_input.sienaFit jn_posterior jn_wald |
| Every figure of a Johnson-Neyman analysis | jn_plots |
| Regions of significance of a Johnson-Neyman analysis | jn_regions jn_regions.JN_2way jn_regions.JN_3way |
| Save the figures of a Johnson-Neyman analysis | jn_save |
| Appearance of Johnson-Neyman figures | jn_style |
| Plot a Johnson-Neyman analysis | autoplot.JN plot.JN plot.JN_list |
| Print a Johnson-Neyman analysis | print.JN |
| Methods for grouped Johnson-Neyman analyses | as.data.frame.JN_list print.JN_list summary.JN_list |
| Summarize a Johnson-Neyman analysis | print.summary.JN summary.JN |
