## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(TOSTER) library(ggplot2) library(ggdist) ## ----------------------------------------------------------------------------- smd_calc(formula = extra ~ group, data = sleep, paired = TRUE, smd_ci = "nct", bias_correction = F) # Setting bootstrap replications low to ## reduce compiling time of vignette boot_smd_calc(formula = extra ~ group, data = sleep, R = 199, paired = TRUE, boot_ci = "stud", bias_correction = F) ## ----fig.width=6, fig.height=6------------------------------------------------ plot_smd(d = .43, df = 58, sigma = .33, smd_label = "Cohen's d", smd_ci = "z" ) ## ----------------------------------------------------------------------------- compare_smd(smd1 = 0.95, n1 = 25, smd2 = 0.23, n2 = 50, paired = TRUE) ## ----------------------------------------------------------------------------- set.seed(4522) diff_study1 = rnorm(25,.95) diff_study2 = rnorm(50) boot_test = boot_compare_smd(x1 = diff_study1, x2 = diff_study2, paired = TRUE) boot_test # Table of bootstrapped CIs knitr::kable(boot_test$df_ci, digits = 4) ## ----------------------------------------------------------------------------- library(ggplot2) list_res = boot_test$boot_res df1 = data.frame(study = c(rep("original",length(list_res$smd1)), rep("replication",length(list_res$smd2))), smd = c(list_res$smd1,list_res$smd2)) ggplot(df1, aes(fill = study, color =smd, x = smd))+ geom_histogram(aes(y=..density..), alpha=0.5, position="identity")+ geom_density(alpha=.2) + labs(y = "", x = "SMD (bootstrapped estimates)") + theme_classic() df2 = data.frame(diff = list_res$d_diff) ggplot(df2, aes(x = diff))+ geom_histogram(aes(y=..density..), alpha=0.5, position="identity")+ geom_density(alpha=.2) + labs(y = "", x = "Difference in SMDs (bootstrapped estimates)") + theme_classic()