Package: designr 0.1.13

designr: Balanced Factorial Designs

Generate balanced factorial designs with crossed and nested random and fixed effects <https://github.com/mmrabe/designr>.

Authors:Maximilian M. Rabe [aut, cre], Reinhold Kliegl [aut], Daniel J. Schad [aut]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
designr/json (API)

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

Bug tracker:https://github.com/mmrabe/designr/issues

Datasets:

On CRAN:

Conda:

5.34 score 10 stars 22 scripts 189 downloads 24 exports 31 dependencies

Last updated from:b462c0c44f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK169
source / vignettesOK254
linux-release-x86_64OK172
macos-release-arm64OK119
macos-oldrel-arm64OK124
windows-develOK112
windows-releaseOK118
windows-oldrelOK135
wasm-releaseOK128

Exports:contrast.namesdesign.codesdesign.contrastsdesign.formuladesign.unitsfactor.designfixed.factorfixed.factorsis.designFactoris.factorDesignis.fixedFactoris.randomFactornobsoutput.designrandom.factorrandom.factorsshowshow.factorContainersimLMMsubsetwrite.designwrite.design.csvwrite.design.jsonwrite.design.xlsx

Dependencies:AlgDesignbootclicrossdesdplyrgenericsgluegtoolslatticelifecyclelme4magrittrMASSMatrixminqanlmenloptrpillarpkgconfigR6rbibutilsRcppRcppEigenRdpackreformulasrlangtibbletidyselectutf8vctrswithr

Power Analyses - Workshop
What is statistical power? | An easy example: 2-sample t-test | Linear model formulation | Content | Steps of a power analysis | a) Create experimental design (desinr) | ANOVA + LMM: Random effects for subjects only, 1 within-subject factor | ANOVA + LMM: Random effects for subjects only, 2x3 within-subject design | LMM: Crossed random effects for subjects and items | Vary effect size | Based on a previous data set: latin square design | LMM: Crossed random effects for subjects and items - with continuous covariate | Logistic GLMM | Custom probability distributions and link functions
Rendered frompower-analyses.Rmdusingknitr::rmarkdown

Last update: 2021-03-06
Started: 2021-02-25

From Design to Dataframe
Setup | Experimental Design(s) | Complete within-subject and within-item design; no counterbalancing | Speed within-subject/within-item, Text within-subject/between-item; no counterbalancing | Age between-subject/within-item, Speed within-subject/within-item; no counterbalancing | Age between-subject/within-item, Speed between-subject/within-item; no counterbalancing | Counterbalancing Speed and Load | Outlook | Appendix | Acknowledgement | Packages
Rendered fromdesign-to-dataframe.Rmdusingknitr::rmarkdown

Last update: 2021-02-25
Started: 2019-10-01

Readme and manuals

Help Manual

Help pageTopics
Concatenate design factors and designs+,factorContainer,factorContainer-method
Retrieve contrast codes for a designcontrast.names design.contrasts
designrdesignr
Factorial Designsfactor.design
Design matrix S4 functionsfactorContainer-class show,factorContainer-method
S4 Methods for designFactorfactorDesign-class
Fixed factorsfixed.factor
Gibson & Wu (2013)gibsonwu2013
Checking factor design data typesis.designFactor is.factorDesign is.fixedFactor is.randomFactor
Retrieve the number of observationsnobs,factorDesign-method
Summary of Factor Designsdesign.codes design.formula design.units output.design
Random factorsrandom.factor
Extract factors by typefixed.factors random.factors
Output a design factor summaryshow.factorContainer
Simulate data from a linear mixed-effects modelsimLMM
Subset factor designssubset subset,factorDesign-method
Write Design Fileswrite.design write.design.csv write.design.json write.design.xlsx