② Users may add a baseline error curve to shared control contrast plots, displayed below the control group. This is the distribution of the bootstrapped mean differences from comparing the control against itself and is a measure of inherent variability within the control group.

We have also added several features not yet present in the Python version of the package: ① As you may have noticed from the images in the thread below, we have adopted asymmetrical swarmplots, thereby better showing the distribution underlying the raw data.

Updated documentation for the R package can be found at: acclab.github.io/dabestr/

Contributors to this update were: Kah Seng LIAN (github.com/sunroofgod), Zhuoyu WANG (@LucasZhuoyu, github.com/Lucas1213WZY), and Jun Yang LIAO (github.com/junyangliao)

④ Last but not least, we introduce the mini-meta plot, a quick way to do meta-analytic averaging from similar experiments. This provides an easy way to summarize results from multiple replicates. This makes meta-analysis of primary data available as a convenient tool.

③ Instead of Fisher’s exact test for the comparison of proportions, you can now use the new DABEST proportion plots. It plots the variance of the observed binary counts and the effect-size curve of the difference of proportions. This function can be used with repeated measures.

For example, ∆∆ plots will be useful for genotype × drug analyses or optogenetic (light × opsin) experiments. A major benefit of the ∆∆ plot is how it places focus on the overall effect size.

② Instead of using a 2-way ANOVA, you can now generate a ∆∆ plot (‘delta-delta plot’) to visualize a 2 × 2 experiment. The ∆∆ shows individual effect sizes for each of the two-groups sub-comparisons, and the overall magnitude of the interaction effect.

Repeated-measures plots enable you to see a series of effect sizes relative to a baseline measurement.

① First up, in dabestr Lapis, a new graphic is suited for repeated-measures effect sizes. You no longer are stuck running repeated-measures ANOVAs, but can instead visualize best-practice estimation plots.

Exciting news for dabestr users! We are releasing dabestr version “Lapis” (v2023.9.12), which adds estimation graphics for four new data types: repeated measures, 2 × 2 designs, proportions, and easy meta-analyses. 🧵

How do dabestr users prefer to get future releases? (2/2) #rstats #DataScience

📢To our R users: We will soon release a new version of dabestr (acclab.github.io/dabestr/) with brand new goodies such as repeated measures, proportion plot, mini-meta and delta-delta. #rstats #datascience We want to know how you prefer to get the package (poll in next tweet): 1/2