Package index • pwrss

Generic Power Analyses

Power analyses based on core distributions

Power analyses for one and two sample (independent and paired) proportions

power.chisq.gof(): Power Analysis for Chi-square Goodness-of-Fit or Independence Tests
power.z.oneprop(): Power Analysis for the Test of One Proportion (Normal Approximation Method)
power.exact.oneprop(): Power Analysis for the Test of One Proportion (Exact Method)
power.z.twoprops() power.z.twoprop(): Power Analysis for Testing the Difference Between Two Proportions (Normal Approximation Method)
power.exact.twoprops() power.exact.twoprop(): Power Analysis for Testing the Difference Between Two Proportions (Exact Method)
power.exact.fisher() power.exact.twoprops.fisher(): Power Analysis for Fisher's Exact Test (Independent Proportions)
power.exact.mcnemar() power.exact.twoprops.mcnemar(): Power Analysis for McNemar's Exact Test (Paired Proportions)

Power analyses for (independent and dependent) correlations

power.z.onecor(): Power Analysis for One-Sample Correlation
power.exact.onecor(): Power Analysis for One-Sample Correlation (Exact)
power.z.twocors() power.z.twocor(): Power Analysis for Independent Correlations
power.z.twocors.steiger() power.z.steiger(): Power Analysis for Dependent Correlations (Steiger's Z-Test)

Power analyses for mean differences (t-tests, Wilcoxon) and ANOVA / ANCOVA models

power.t.student(): Power Analysis for Student's t-Test
power.t.welch(): Power Analysis for Welch's t-Test
power.np.wilcoxon() power.np.wilcox(): Power Analysis for Non-parametric Rank-Based Tests (One-Sample, Independent, and Paired Designs)
power.f.ancova(): Power Analysis for One-, Two-, Three-Way ANOVA/ANCOVA Using Effect Size (F-Test)
power.f.ancova.keppel(): Power Analysis for One-Way ANOVA/ANCOVA Using Means and Standard Deviations (F test)
power.f.ancova.shieh(): Power Analysis for One-, Two-, Three-Way ANCOVA Using Means, Standard Deviations, and (Optionally) Contrasts (F test)
factorial.contrasts(): Factorial Contrasts
power.t.contrast(): Power Analysis for One-, Two-, Three-Way ANCOVA Contrasts and Multiple Comparisons (T-Tests)
power.t.contrasts(): Power Analysis for One-, Two-, Three-Way ANCOVA Contrasts and Multiple Comparisons (T-Tests)
power.f.mixed.anova(): Power Analysis for Mixed-Effects Analysis of Variance (F-Test)

Power analyses for linear, logistic, and Poisson regressions

power.f.regression() power.f.reg(): Power Analysis for Linear Regression: R-squared or R-squared Change (F-Test)
power.t.regression() power.t.reg(): Power Analysis for Linear Regression: Single Coefficient (T-Test)
power.z.mediation() power.z.med(): Power Analysis for Indirect Effects in a Mediation Model (Z, Joint, and Monte Carlo Tests)
power.z.logistic() power.z.logreg(): Power Analysis for Logistic Regression Coefficient (Wald's Z-Test)
power.z.poisson() power.z.poisreg(): Power Analysis for Poisson Regression Coefficient (Wald's z Test)

Helper functions, and functions to transform and convert effect size metrics

inflate.sample(): Inflate Sample Size for Attrition
d.to.cles() cles.to.d(): Conversion from Cohen's d to Common Language Effect Size
cor.to.z(): Conversion from a correlation to a z-value (Fisher's z-transformation)
cors.to.q(): Conversion from a correlation Difference to Cohen's q
etasq.to.f(): Conversion from Eta-squared to Cohen's f
f.to.etasq(): Conversion between Cohen's f and Eta-squared
f.to.rsq(): Conversion from Cohen's f to R-squared
joint.probs.2x2(): Conversion from joint probabilities to marginal probabilities for the McNemar test applied to paired binary data.
marginal.probs.2x2(): Conversion from marginal probabilities to joint probabilities for the McNemar test applied to paired binary data.
means.to.d(): Conversion from Means and Standard Deviations to Cohen's d
means.to.etasq(): Conversion from Means and Standard Deviations to Cohen's f and Eta-squared
probs.to.h(): Conversion from Probability Difference to Cohen's h
probs.to.w(): Conversion from Probabilities to Cohen's w
q.to.cors(): Conversion from a Cohen's q to a correlation difference
rsq.to.f(): Conversion from R-squared to Cohen's f
z.to.cor(): Conversion from a z-value to a correlation (inverse Fisher's z-transformation)

Distribution functions for the lambda prime distribution

dlambdap() plambdap() qlambdap() rlambdap(): Distribution functions for the Lambda prime / non-central Lambda distribution