Package: pwr2ppl 0.5.0

pwr2ppl: Power Analyses for Common Designs (Power to the People)

Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.

Authors:Chris Aberson

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# Install 'pwr2ppl' in R:
install.packages('pwr2ppl', repos = c('https://chrisaberson.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/chrisaberson/pwr2ppl/issues

On CRAN:

4.16 score 17 stars 17 scripts 297 downloads 56 exports 87 dependencies

Last updated 2 years agofrom:06a0366cf8. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winWARNINGNov 05 2024
R-4.5-linuxWARNINGNov 05 2024
R-4.4-winWARNINGNov 05 2024
R-4.4-macWARNINGNov 05 2024
R-4.3-winWARNINGNov 05 2024
R-4.3-macWARNINGNov 05 2024

Exports:ancanova1f_3anova1f_3canova1f_4anova1f_4canova2x2anova2x2_seAssumptionsAssumptions_resampleChi2x2Chi2X3ChiESChiGOFcorrd_precdepbdepcorr0depcorr1indbindcorrindR2indtlmm1Flmm1Ftrendslmm1w1blmm2Flmm2FseLRcatLRcontMANOVA1fmd_precmedmedjsmedjs_pathsmedserialmedserial_pathsmodmed14modmed7MRCMRC_allMRC_short2MRC_shortcutspairtprop1propindr_precR2_precR2chregintregintR2tfromdwin1bg1win1Fwin1Ftrendswin2Fwin2Fse

Dependencies:abindafexarmbackportsBHbootbroomcarcarDataclicodacolorspacecowplotcpp11DerivdigestdoBydplyrezfansifarverFormulagenericsggplot2gluegtableisobandlabelinglatticelavaanlifecyclelme4lmerTestlmPermlmtestmagrittrMASSMatrixMatrixModelsMBESSmgcvmimicrobenchmarkminqamnormtmodelrmunsellmvtnormnlmenloptrnnetnumDerivOpenMxpbivnormpbkrtestphiapillarpkgconfigplsplyrpurrrquadprogquantregR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrpfscalessemsemToolsSparseMStanHeadersstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Compute Power for One or Two Factor ANCOVA with a single covariate Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user Factor A can have up to four levels, Factor B, if used, can only be twoanc
Compute power for a One Factor ANOVA with three levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by useranova1f_3
Compute power for a One Factor ANOVA with three levels and contrasts. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by useranova1f_3c
Compute power for a One Factor Between Subjects ANOVA with four levels Takes means, sds, and sample sizes for each groupanova1f_4
Compute power for a One Factor ANOVA with four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by useranova1f_4c
Compute power for a Two by Two Between Subjects ANOVA. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by useranova2x2
Compute power for Simple Effects in a Two by Two Between Subjects ANOVA with two levels for each factor. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by useranova2x2_se
Compute power for Multiple Regression with Violated assumptions (Beta)Assumptions
Compute power for Multiple Regression with Violated assumptions using ResamplesAssumptions_resample
Compute power for an Chi Square 2x2 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by userChi2x2
Compute power for an Chi Square 2x3 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by userChi2X3
Compute power for Chi Square Based on Effect Size Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by userChiES
Compute power for an Chi Square Goodness of Fit Takes proportions for up to six group. Alpha is .05 by default, alternative values may be entered by userChiGOF
Compute power for Pearson's Correlation Takes correlation and range of valuescorr
Compute Precision Analyses for Standardized Mean Differencesd_prec
Power for Comparing Dependent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)depb
Compute Power for Comparing Two Dependent Correlations, No Variables in Common Takes correlations and range of values. First variable in each pair is termed predictor, second is DVdepcorr0
Compute Power for Comparing Two Dependent Correlations, One Variable in Common Takes correlations and range of valuesdepcorr1
Power for Comparing Independent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)indb
Compute Power for Comparing Two Independent Correlations Takes correlations and range of valuesindcorr
Power for Comparing Independent R2 in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)indR2
Compute power for an Independent Samples t-test Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userindt
Compute power for a One Factor Within Subjects Linear Mixed Model with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userlmm1F
Compute power for a One Factor Within Subjects LMM Trends with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userlmm1Ftrends
Compute power for a One Factor Within Subjects and One Factor Between LMM with up to two by four levels (within). Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userlmm1w1b
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userlmm2F
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userlmm2Fse
Compute Power for Logistic Regression with a Single Categorical PredictorLRcat
Compute Power for Logistic Regression with Continuous PredictorsLRcont
Compute power for a One Factor MANOVA with up to two levels and up to four measures. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userMANOVA1f
Compute Precision Analyses for Mean Differencesmd_prec
Compute Power for Mediated (Indirect) Effects Requires correlations between all variables as sample size. This approach calculates power for the Sobel test. The medjs function calculates power based on joint significance (recommended)med
Compute Power for Mediated (Indirect) Effects Using Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining powermedjs
Compute Power for Mediated (Indirect) Effects Using Joint Significance Requires paths for all effects (and if 2 mediators, correlation) Standard deviations/variances set to 1.0 so paths are technically standardizedmedjs_paths
Compute Power for Serial Mediation Effects Requires correlations between all variables as sample size. This approach calculates power for the serial mediation using joint significance (recommended)medserial
Compute Power for Serial Mediation Effects Requires correlations between all variables as sample size. This approach calculates power for the serial mediation using joint significance (recommended) and path coefficientsmedserial_paths
Compute Power for Conditional Process Model 14 Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining powermodmed14
Compute Power for Model 7 Conditional Processes Using Joint Significance Requires correlations between all variables as sample size Several values default to zero if no value provided This is the recommended approach for determining powermodmed7
Compute power for Multiple Regression with up to Five Predictors Example code below for three predictors. Expand as needed for four or fiveMRC
Compute power for Multiple Regression with Up to Five Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)MRC_all
Compute Multiple Regression shortcuts with three predictors for Ind Coefficients Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)MRC_short2
Compute Multiple Regression shortcuts with three predictors (will expand to handle two to five) Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)MRC_shortcuts
Compute power for a Paired t-test Takes means, sd, and sample sizes. Alpha is .05 by default, alternative values may be entered by user. correlation (r) defaults to .50.pairt
Compute power for a single sample proportion test Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by userprop1
Compute power for Tests of Two Independent Proportions Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user This test uses what is sometimes called the chi-square test for comparing proportionspropind
Compute Precision Analyses for Correlations This approach simply loops a function from MBESSr_prec
Compute Precision Analyses for R-Squared This approach simply loops a function from MBESSR2_prec
Compute power for R2 change in Multiple Regression (up to three predictors) Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All) Example code below for three predictors. Expand as needed for four or fiveR2ch
Compute Power for Regression Interaction (Correlation/Coefficient Approach)regint
Compute Power for Regression Interaction (R2 Change Approach)regintR2
Compute power for a t test using d statistic Takes d, sample size range, type of test, and tails.tfromd
Compute power for a One Factor Within Subjects and One Factor Between ANOVA with up to two by four levels (within). Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userwin1bg1
Compute power for a One Factor Within Subjects ANOVA with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userwin1F
Compute power for a One Factor Within Subjects Trends with up to four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userwin1Ftrends
Compute power for a Two Factor Within Subjects ANOVA with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userwin2F
Compute power for Simple Effects in Two Factor Within Subjects ANOVA with up to two by four levels. Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by userwin2Fse