Package: Superpower 0.2.4.2

Superpower: Simulation-Based Power Analysis for Factorial Designs

Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.

Authors:Aaron Caldwell [aut, cre], Daniel Lakens [aut], Lisa DeBruine [ctb], Jonathon Love [ctb], Frederik Aust [ctb]

Superpower_0.2.4.2.tar.gz
Superpower_0.2.4.2.zip(r-4.7)Superpower_0.2.4.2.zip(r-4.6)Superpower_0.2.4.2.zip(r-4.5)
Superpower_0.2.4.2.tgz(r-4.6-any)Superpower_0.2.4.2.tgz(r-4.5-any)
Superpower_0.2.4.2.tar.gz(r-4.7-any)Superpower_0.2.4.2.tar.gz(r-4.6-any)
Superpower_0.2.4.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Superpower/json (API)

# Install 'Superpower' in R:
install.packages('Superpower', repos = c('https://arcaldwell49.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/arcaldwell49/superpower/issues

On CRAN:

Conda:

9.06 score 68 stars 137 scripts 1.0k downloads 2 mentions 23 exports 76 dependencies

Last updated from:c606a92d76. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK241
source / vignettesOK327
linux-release-x86_64OK229
macos-release-arm64OK145
macos-oldrel-arm64OK174
windows-develOK222
windows-releaseOK167
windows-oldrelOK173
wasm-releaseOK133

Exports:alpha_standardizedANCOVA_analyticANCOVA_contrastANOVA_compromiseANOVA_designANOVA_exactANOVA_exact2ANOVA_poweremmeans_powermorey_plot.ftestmorey_plot.ttestmu_from_ESoptimal_alphap_standardizedplot_powerpower_oneway_ancovapower_oneway_betweenpower_oneway_withinpower_standardized_alphapower_threeway_betweenpower_twoway_betweenpower.ftestSuperpower_options

Dependencies:abindafexbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyremmeansestimabilityfarverforecastFormulafracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmerTestlmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

More ANOVA designs
Three between subject conditions | Power in Repeated Measures ANOVA | Two within conditions | Power in Repeated Measures ANOVA with More than 2 Groups | The relation between Cohen's f and Cohen's d | 2x2 ANOVA, within-between design | Two-way ANOVA, between participants Design | 3x3 Between Subject ANOVA | Two by two ANOVA, within design
Rendered frommore_anova_designs.Rmdusingknitr::rmarkdown

Last update: 2024-12-30
Started: 2021-03-19

Power Calculations for ANCOVA
Introduction | One-way ANCOVA | @cohen_book Method | @shieh_ancova Method | Extending to factorial ANOVAs | ANCOVA_analytic | Example: 2x3 Design | Contrasts | References
Rendered fromANCOVAs.Rmdusingknitr::rmarkdown

Last update: 2024-10-22
Started: 2021-12-09

Introduction to Superpower
Installation | Superpower_options | ANOVA_design function | Specifying the design using 'design' | Specifying the means using 'mu' | Specifying label names | Specifying the correlation | Specifying the correlation matrix. | Specifying the sample size | Specifying the standard deviation | Simulation-Based Power Calculations | The ANOVA_power function | The ANOVA_exact function | An Example | Power in a Two Group One-Way ANOVA | Plotting Power | The plot_power function | Power-Sensitivity Relationship: Morey Plots
Rendered fromintro_to_superpower.Rmdusingknitr::rmarkdown

Last update: 2021-12-09
Started: 2019-09-06

Power calculations for emmeans analyses
A note of caution | Post hoc pairwise comparisons | Customized emmeans contrasts | Equivalence and non-superiority/-inferiority tests | Joint tests
Rendered fromemmeans_power.Rmdusingknitr::rmarkdown

Last update: 2021-12-09
Started: 2020-04-22

Introduction to Justifying Alpha Levels
Introduction | How are error rates minimized? | Minimizing Error Rates | Balancing Error Rates | Relative costs and prior probabilities | Extending to the Analysis of Variance | An Example with ANOVA_exact2 | ANOVA_compromise
Rendered fromcompromise_power.Rmdusingknitr::rmarkdown

Last update: 2021-03-19
Started: 2020-08-26

Readme and manuals

Help Manual

Help pageTopics
Compute standardized alpha level based on unstandardized alpha level and the number of observations N.alpha_standardized
Power Calculations for Factorial ANCOVAsANCOVA_analytic
Power Calculations for ANCOVA ContrastsANCOVA_contrast
Methods for ancova_power objectsancova_power-methods plot.ancova_power print.ancova_power
Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates for ANOVAs.ANOVA_compromise
Design function used to specify the parameters to be used in simulationsANOVA_design
Simulates an exact dataset (mu, sd, and r represent empirical, not population, mean and covariance matrix) from the design to calculate powerANOVA_exact ANOVA_exact2
Simulation function used to estimate powerANOVA_power
Methods for design_aov objectsdesign_aov-methods plot.design_aov print.design_aov
Compute power for 'emmeans' contrastsemmeans_power emmeans_power.data.frame emmeans_power.emmGrid emmeans_power.summary_em
Plot out power sensitivity plots for t or F testsmorey_plot.ftest morey_plot.ttest
Convenience function to calculate the means for between designs with one factor (One-Way ANOVA). Can be used to determine the means that should yield a specified effect sizes (expressed in Cohen's f).mu_from_ES
Methods for opt_alpha objectsopt_alpha-methods plot.opt_alpha print.opt_alpha
Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates.optimal_alpha
Compute standardized alpha level based on unstandardized alpha level and the number of observations N.p_standardized
Convenience function to plot power across a range of sample sizes.plot_power
Power Calculations for a one-way ANCOVApower_oneway_ancova
Analytic power calculation for one-way between designs.power_oneway_between
Analytic power calculation for one-way within designs.power_oneway_within
Optimizing function to achieve desired power based on a standardized alpha level.power_standardized_alpha
Analytic power calculation for three-way between designs.power_threeway_between
Analytic power calculation for two-way between designs.power_twoway_between
Power Calculations for an F-testpower.ftest
Methods for sim_result objectsconfint.sim_result plot.sim_result print.sim_result sim_result-methods
Set/get global Superpower optionsSuperpower_options