{"version":"1.0","type":"rich","title":"Gist b989c26735fde8214c19","description":"This example demonstrates statistical power calculations for several genetic association models (DRAB1_sGMV, HLAB_ucca, HLAB_sGM, NFkb_WM) using R’s noncentral F distribution via the rpy2 interface, with power derived from effect sizes and sample sizes listed in a pandas DataFrame. It imports the `stats` package and uses `statsmodels.api` alongside scipy.stats, defining a custom `pf` function that calls R’s `qf` and `pf` functions to compute critical values and noncentral power. The notebook also loads precomputed intraclass correlation coefficients from a pickled file and displays the power table as an HTML-rendered pandas DataFrame.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-b989c26735fde8214c19?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}