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

knitr::opts_chunk$set(echo = TRUE)

Problem 1: Repeated Measurements Data

if (params$isonline){
  s_asm_ex08_p01_data_path <- "https://charlotte-ngs.github.io/asmss2022/data/asm_bw_flem.csv"
} else {
  s_asm_ex08_p01_data_path <- file.path(here::here(), "docs", "data", "asm_bw_flem.csv")
}
n_nr_rep <- 5
n_sd_prop_bw <- 0.5

Simulate a dataset with repeated measurements of Body Weight and Breed. The following dataset can be used as a basis:

cat(s_asm_ex08_p01_data_path, "\n")

The generated dataset should have the following properties

• For every observation, the ID of the animal, its Body Weight and its Breed should be contained in the dataset.
• Each animal of the given basis dataset should have r n_nr_rep repeated observations of Body Weight and Breed.
• The phenotypic variance of Body Weight within the repeated observations of one animal should be r 100 * n_sd_prop_bw% of the total phenotypic variance of Body Weight determined from the given basis dataset.

Your Tasks

• Analyse the generated dataset with an ANOVA
• Try to see whether you can re-cover the used input data in the results of the analysis

Your Solution

• Read the given basis dataset
• Loop over the records in the basis dataset and add the required number of records
• Analyse the generated dataset with an ANOVA
• Assess the results and compare them with the input used in the simulation

Problem 2: Random Effects Model

s_ex08_p02_data_path <- "https://charlotte-ngs.github.io/asmss2022/data/asm_ex08_p01_rep_obs.csv"
if (!params$isonline)
  s_ex08_p02_data_path <- file.path(here::here(), "docs", "data", "asm_ex08_p01_rep_obs.csv")

Analyse the dataset generated in Problem 1 with a random effects model using the package lme4. If you had difficulties to solve Problem 1, then you can also use the following dataset.

cat(s_ex08_p02_data_path, "\n")

Your Solution

• Read generated dataset from Problem 1
• Analyse the data using lme4::lmer()

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.