Package: ANOPA 0.2.3

ANOPA: Analyses of Proportions using Anscombe Transform

Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed data. The 'ANOPA' package can analyze proportions obtained from up to four factors. The factors can be within-subject or between-subject or a mix of within- and between-subject. The main, omnibus analysis can be followed by additive decompositions into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For that reason, we call this set of tools 'ANOPA' (Analysis of Proportion using Anscombe transform) to highlight its similarities with ANOVA. The 'ANOPA' framework also allows plots of proportions easy to obtain along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. Only particularity, the 'ANOPA' computes F statistics which have an infinite degree of freedom on the denominator. See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.

Authors:Denis Cousineau [aut, ctb, cre], Louis Laurencelle [aut, ctb]

ANOPA_0.2.3.tar.gz
ANOPA_0.2.3.zip(r-4.7)ANOPA_0.2.3.zip(r-4.6)ANOPA_0.2.3.zip(r-4.5)
ANOPA_0.2.3.tgz(r-4.6-any)ANOPA_0.2.3.tgz(r-4.5-any)
ANOPA_0.2.3.tar.gz(r-4.7-any)ANOPA_0.2.3.tar.gz(r-4.6-any)
ANOPA_0.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
ANOPA/json (API)

# Install 'ANOPA' in R:
install.packages('ANOPA', repos = c('https://dcousin3.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dcousin3/anopa/issues

Pkgdown/docs site:https://dcousin3.github.io

Datasets:

On CRAN:

Conda:

error-barsproportionsstatistical-testingstatisticssummary-statistics

5.18 score 1 stars 17 scripts 552 downloads 26 exports 53 dependencies

Last updated from:9f79a64a0d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK179
source / vignettesOK203
linux-release-x86_64OK171
macos-release-arm64OK124
macos-oldrel-arm64OK103
windows-develOK132
windows-releaseOK133
windows-oldrelOK124
wasm-releaseOK148

Exports:AanopaanopaN2PoweranopaPlotanopaPower2NanopaProp2fsqAtransCI.AtransCI.propcontrastProportionscorrectedemProportionsexplainGRPposthocProportionsproprBernoulliSE.AtranssummarizetoCompiledtoLongtoWideuncorrectedunitaryAlphavar.AtransvarA

Dependencies:base64encbslibcachemclicommonmarkcpp11digestfarverfastmapfontawesomeforeignfsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlifecyclelsrmagrittrMASSmemoisemimeotelplyrpromisesR6rappdirsrbibutilsRColorBrewerRcppRdpackreshape2rlangrrapplyS7sassscalesshinyshinyBSsourcetoolsstringistringrsuperbvctrsviridisLitewithrxtable

What is an Analysis of Proportions using the Anscombe Transform?
A basic example | The rational behind the test (optional) | Analyzing the data | Post-hoc test | A common confusion | Why infinite degrees of freedom? (optional) | Why the arcsine transform? (optional) | References

Last update: 2025-07-18
Started: 2024-03-17

Analyzing proportions with the Arrington et al. 2002 example
References

Last update: 2025-01-19
Started: 2024-03-17

Data formats for proportions
First format: Wide data format | Second format: Long data format | Third format: Compiled data format | Converting between formats | Getting the example data frame | Multiple repeated-measure factors | References

Last update: 2025-01-19
Started: 2024-03-17

Confidence intervals with proportions
Theory behind Confidence intervals for proportions | Complicated? | References

Last update: 2024-11-30
Started: 2024-03-17

Is the ArcSine transformation so asinine in the end?
References

Last update: 2024-11-30
Started: 2024-03-17

Testing type-I error rates
Simulations with a single factor | Simulation with one between factor | Simulation with one within factor | Simulations with two factors | Simulation with two factors, between design | Simulation with two factors, within design | Simulation with two factors, mixed design | Simulations with three factors | Simulation with three factors, all between design | Simulation with three factors, within design | Simulation with three factors, mixed design, testing within | Simulation with three factors, mixed design, testing between | The end

Last update: 2024-11-30
Started: 2024-03-17

Readme and manuals

Help Manual

Help pageTopics
ANOPA: analysis of proportions using Anscombe transform.anopa anopafct
anopaPlot: Easy plotting of proportions.anopaPlot
Arrington et al. (2002) datasetArringtonEtAl2002
ArticleExample1ArticleExample1
ArticleExample2ArticleExample2
ArticleExample3ArticleExample3
contrastProportions: analysis of contrasts between proportions using Anscombe transform.contrastProportions
Converting between formatsConversion toCompiled toLong toWide
correctedcorrected
emProportions: simple effect analysis of proportions.emProportions
explainexplain
Generating random proportions with GRPGRP gSwitch rBernoulli
A collection of minimal Examples from various designs with one or two factors.minimalBSExample minimalExamples minimalMxExample minimalMxExampleCompiled minimalWSExample twoWayExample twoWayWithinExample
posthocProportions: post-hoc analysis of proportions.posthocProportions
Computing power within the ANOPA.anopaN2Power anopaPower2N anopaProp2fsq PowerComputation
summarizesummarize
Transformation functionsA Atrans CI.Atrans CI.prop prop SE.Atrans Transformations var.Atrans varA
uncorrecteduncorrected
unitary alphaunitaryAlpha