# pwr2ppl v0.1.1

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## 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>.

## Functions in pwr2ppl

 Name Description anova1f_3 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 user r_prec Compute Precision Analyses for Correlations This approach simply loops a function from MBESS win1F 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 user anova2x2_se 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 user anova2x2 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 user propind 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 tfromd Compute power for a t test using d statistic Takes d, sample size range, type of test, and tails. indt 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 user anova1f_3c 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 user lmm1F 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 user MRC_shortcuts 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) d_prec Compute Precision Analyses for Standardized Mean Differences depb 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) R2_prec Compute Precision Analyses for R-Squared This approach simply loops a function from MBESS med Compute Power for Mediated (Indirect) Effects Requires correlations between all variables as sample size. depcorr0 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 DV prop1 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 user pairt 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. md_prec Compute Precision Analyses for Mean Differences R2ch 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 five Chi2x2 Compute power for an Chi Square 2x2 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user lmm2F 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 user anova1f_4 Compute power for a One Factor Between Subjects ANOVA with four levels Takes means, sds, and sample sizes for each group LRcont Compute Power for Logistic Regression with Continuous Predictors lmm1Ftrends 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 user anova1f_4c 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 user indb 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) indcorr Compute Power for Comparing Two Independent Correlations Takes correlations and range of values win2F 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 user win2Fse 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 user lmm1w1b 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 user anc 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 depcorr1 Compute Power for Comparing Two Dependent Correlations, One Variable in Common Takes correlations and range of values indR2 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) lmm2Fse 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 user regintR2 Compute Power for Regression Interaction (R2 Change Approach) regint Compute Power for Regression Interaction (Correlation/Coefficient Approach) win1Ftrends 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 user win1bg1 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 user MRC_short2 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) MANOVA1f 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 user Chi2X3 Compute power for an Chi Square 2x3 Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user ChiES 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 user LRcat Compute Power for Logistic Regression with a Single Categorical Predictor ChiGOF 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 user MRC_all 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 Compute power for Multiple Regression with up to Five Predictors Example code below for three predictors. Expand as needed for four or five corr Compute power for Pearson's Correlation Takes correlation and range of values No Results!