Statistical Rethinking 2024: Lecture 5
Figures and models shown during the lecture Elemental Confounds
'%3e%3cg%20transform='matrix(0.12928,0,0,0.12928,4056.614702,2455.921073)'%3e%3cg%20transform='matrix(1,0,0,1,-1041.666667,-1041.666667)'%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M249.98,46C137.31,46%2046,137.353%2046,250.02C46,303.229%2066.377,351.67%2099.74,387.98C124.466,380.486%20149.437,373.515%20169.773,368.205C214.439,356.54%20283.163,338.753%20320.063,316.547C307.902,308.752%20287.034,300.28%20237.522,284.615C187.969,268.937%20133.012,252.361%20136.672,235.244C136.672,235.244%20136.804,234.766%20137.222,233.967C137.112,234.144%20136.94,234.31%20136.836,234.488C145.016,213.582%20169.729,158.832%20169.729,158.832C187.454,129.745%20265.922,108.936%20320.659,93.809C333.426,90.281%20348.675,85.949%20364.721,81.301C332.029,59.022%20292.521,45.998%20249.971,45.998L249.98,46ZM431.66,157.15C394.847,163.09%20278.71,184.171%20248.95,190.886C236.841,193.618%20220.205,197.188%20203.38,201.726C227.174,213.428%20234.595,219.28%20267.48,231.427C300.423,243.596%20349.822,271.346%20344.32,293.048C344.32,293.048%20344.083,293.676%20343.687,294.558C339.467,306.15%20326.097,335.964%20326.097,335.964C307.463,354.143%20222.777,374.304%20167.017,385.036C149.23,388.459%20128.928,392.416%20108.343,396.778C145.031,432.186%20194.955,453.998%20249.973,453.998C362.643,453.998%20453.993,362.685%20453.993,250.018C453.993,216.571%20445.923,185.006%20431.655,157.15L431.66,157.15ZM170.2,212.377C169.655,212.591%20169.122,212.808%20168.585,213.025C169.107,212.81%20169.66,212.593%20170.2,212.377M164.45,214.785C163.027,215.405%20161.638,216.036%20160.284,216.678C161.574,216.058%20163.015,215.421%20164.45,214.785M159.352,217.137C154.824,219.33%20150.757,221.651%20147.26,224.107C150.329,221.975%20154.26,219.633%20159.352,217.137M145.424,225.434C144.848,225.874%20144.246,226.309%20143.71,226.758C144.227,226.333%20144.829,225.882%20145.424,225.434'%20style='fill:rgb(65,65,65);fill-rule:nonzero;'/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/svg%3e){kind=small}
Stan '%3e%3cg%20transform='matrix(0.350002,0,0,0.350002,5868.906928,2674.234751)'%3e%3cg%20transform='matrix(1,0,0,1,-1508.333333,-1168.75)'%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M361.453,485.937C162.329,485.937%200.906,377.828%200.906,244.469C0.906,111.109%20162.329,3%20361.453,3C560.578,3%20722,111.109%20722,244.469C722,377.828%20560.578,485.937%20361.453,485.937ZM416.641,97.406C265.289,97.406%20142.594,171.314%20142.594,262.484C142.594,353.654%20265.289,427.562%20416.641,427.562C567.992,427.562%20679.687,377.033%20679.687,262.484C679.687,147.971%20567.992,97.406%20416.641,97.406Z'%20style='fill:rgb(140,140,140);'/%3e%3c/g%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M550,377C550,377%20571.822,383.585%20584.5,390C588.899,392.226%20596.51,396.668%20602,402.5C607.378,408.212%20610,414%20610,414L696,559L557,559.062L492,437C492,437%20478.69,414.131%20470.5,407.5C463.668,401.969%20460.755,400%20454,400L420.974,400L421,558.974L298,559.026L298,152.938L545,152.938C545,152.938%20657.5,154.967%20657.5,262C657.5,369.033%20550,377%20550,377ZM496.5,241.024L422.037,240.976L422,310.026L496.5,310.002C496.5,310.002%20531,309.895%20531,274.877C531,239.155%20496.5,241.024%20496.5,241.024Z'%20style='fill:rgb(64,64,64);'/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/svg%3e){kind=small}
R '%3e%3cg%20transform='matrix(2.62,0,0,2.62,5476.682446,2950.142713)'%3e%3cg%20transform='matrix(1,0,0,1,-50,-50)'%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M9.86,2C8.291,2%207,3.291%207,4.86L7,6.54L11.29,6.54C11.68,6.54%2012,7.11%2012,7.5L4.86,7.5C3.291,7.5%202,8.791%202,10.36L2,14.141C2,15.71%203.291,17.001%204.86,17.001L6.04,17.001L6.04,14.321C6.04,14.318%206.04,14.314%206.04,14.311C6.04,12.748%207.327,11.461%208.89,11.461C8.89,11.461%2014.14,11.461%2014.14,11.461C15.72,11.461%2017,10.19%2017,8.61L17,4.86C17,3.291%2015.709,2%2014.14,2L9.86,2ZM9.14,3.61C9.54,3.61%209.86,3.73%209.86,4.32C9.86,4.91%209.54,5.211%209.14,5.211C8.75,5.211%208.43,4.911%208.43,4.321C8.43,3.731%208.75,3.61%209.14,3.61'%20style='fill:rgb(106,106,106);fill-rule:nonzero;'/%3e%3c/g%3e%3cg%20transform='matrix(4.166667,0,0,4.166667,0,0)'%3e%3cpath%20d='M17.959,7L17.959,9.68C17.959,9.683%2017.959,9.686%2017.959,9.689C17.959,11.252%2016.672,12.539%2015.109,12.539C15.109,12.539%209.86,12.539%209.86,12.539C9.857,12.539%209.853,12.539%209.85,12.539C8.287,12.539%207,13.826%207,15.389C7,15.389%207,19.139%207,19.139C7,20.708%208.291,21.999%209.86,21.999L14.14,21.999C15.709,21.999%2016.999,20.709%2017,19.14L17,17.46L12.709,17.46C12.319,17.46%2012,16.89%2012,16.5L19.14,16.5C20.709,16.5%2022,15.209%2022,13.64L22,9.86C22,8.291%2020.709,7%2019.14,7L17.959,7ZM8.32,11.513L8.316,11.517L8.354,11.513L8.32,11.513ZM14.86,18.789C15.25,18.789%2015.57,19.089%2015.57,19.679C15.57,20.068%2015.249,20.389%2014.86,20.389C14.46,20.389%2014.14,20.269%2014.14,19.679C14.14,19.089%2014.46,18.789%2014.86,18.789'%20style='fill:rgb(153,153,153);fill-rule:nonzero;'/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/svg%3e){kind=small}
Python '%3e%3ccircle%20cx='13.497'%20cy='281.63'%20r='9.555'%20fill='%23c62828'/%3e%3ccircle%20cx='36.081'%20cy='281.63'%20r='9.555'%20fill='%237e57c2'/%3e%3ccircle%20cx='24.722'%20cy='262.39'%20r='9.555'%20fill='%23388e3c'/%3e%3c/g%3e%3c/svg%3e){kind=small}
Julia This notebook reproduces all the figures and code blocks shown during the Elemental Confounds lecture from the 2024 lecture series.
Each code snippet comes with a time-tagged YouTube link, so you can jump right over to YouTube if you’d like to see what Richard says about the code.
The notebook touches on the following topics:
- Simulating data
- Different types of confounding
- Animating plots in Makie 1
Footnotes
-
You need to download and run the notebook to see the animation. ↩