In charlotte-ngs/asmss2022: Applied Statistical Methods in Animal Science - Spring Semester 2022

knitr::opts_chunk$set(echo = TRUE)

Problem 1: Milk Dataset

Use the dataset milk from package pedigreemm and fit a sire model to each of the response variables (milk, fat, prot and scs) in the data. The dataset can be loaded using the command pedigreemm::milk. The other variables like lact and herd can be used as fixed effects. The sire column is used as a random effect. For this analysis, we assume that sires are unrelated.

Your Tasks

• Analyse the milk dataset from package pedigreemm using the function lme4::lmer() for all given response variables. You can use the same model for each of the responses.
• Compute the estimated heritability for each response variable, using the fact that the heritability $h^2$ can be computed from the variance $\sigma_s^2$ of the sire effects and the phenotypic variance $\sigma_p^2$ with the formula

$$h^2 = \frac{4* \sigma_s^2}{\sigma_p^2} $$

• Compute the summary statistic using the function summary() of all the predicted sire breeding values. Solutions for the sire breeding values are obtained using the function ranef()

Your Solution

• Assign milk dataset to a tibble
• Analyse the data for each of the responses
• Compute estimated heritability
• Obtain summary statistics for predicted sire breeding values

cat('\n---\n\n _Latest Changes: ', format(Sys.time(), '%Y-%m-%d %H:%M:%S'), ' (', Sys.info()['user'], ')_\n', sep = '')

charlotte-ngs/asmss2022 documentation built on June 7, 2022, 1:33 p.m.