𝕨ork.dir = "~/Downloads/tempo/" knitr::opts_knit$set(root.dir = normalizePath(work.dir)) library(tidyverse) ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ── ## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4 ## ✓ tibble 3.1.6 ✓ dplyr 1.0.7 ## ✓ tidyr 1.1.4 ✓ stringr 1.4.0 … Read more →

𝕙ttps://www.icloud.com/keynote/0b9pu78RBdrSGsSRVCUWkNahg#2022-L7-Population-Structure I’ve also uploaded a powerpoint version on canvas Module/lecture 7 as a backup. […] Mills, Melinda C.; Barban, Nicola; Tropf, Felix C.. An Introduction to Statistical Genetic Data Analysis (p. 237). … Read more →

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𝕀n this problem, you will simulate a binary outcome Y and genotype X for 100 individuals (nsamp) in 1000 scenarios (nsim). Set the minor allele frequency of X to be 0.30 (maf). Work on an R markdown document and turn in the html generated by knitting with R […] (10 pts.) For the first 100 … Read more →

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𝕋he 2022 syllabus has all the relevant information. […] We’ll do a round of introductions. Each person will tell us the following […] Review of concepts in probabiliy and statistics […] Review probability […] Maximum Likelihood Estimation […] Bayesian Inference … Read more →

𝕎e are told that 241,945 girls and 251,527 boys were born in Paris from 1745 to 1770. y = 241945 n = 241945 + 251527 posterior = function(theta) dbeta(theta,shape1 = y+1, shape2 = n- y + 1) curve(posterior,from = 0,to = 1, main=paste("n = ",n,"; y = … Read more →

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𝕟 = 10 y = 8 likefun = function(theta) {theta^y * (1 - theta)^(n-y)} curve(likefun,from = 0,to = 1, main=paste("n = ",n,"; y = ",y),xlab="theta", ylab="likelihood") abline(v=y/n,col='gray') What if we got 10 heads? n = 10 y = 10 likefun = … Read more →