Applied Statistical Methods - Notebook 4

Problem 1: Sum Contrasts

Use the following dataset on Body Weight and Breed of beef cattle animals. The data is available from

[1] "https://charlotte-ngs.github.io/asmss2023/data/asm_bw_breed.csv"

https://charlotte-ngs.github.io/asmss2023/data/asm_bw_breed.csv

Fit a fixed linear model with Body Weight as response and Breed as predictor variable. Use the sum contrasts for reporting the different effects in the model. Validate the estimates by computing the estimates based on a solution of the least squares normal equations.

Your Solution

• Read the dataset given by the above link
• Change contrasts for the factor Breed to sum contrasts
• Fit linear model with Body Weight as response and Breed as predictor
• Compute solutions of least squares normal equation
• Use contrasts matrix to determine the estimable functions
• Compare values of estimable functions to output of lm()

Problem 2: Custom Contrasts

Use the dataset from Problem 1 and use your own contrasts. Your new contrasts should compute the intercept estimate as is done in the sum contrasts. The Breed effects should be computed the same way as is done in the treatment contrast.

Your Solution

• Construct the matrix of estimable functions and of the linear combination for the intercept
• Compute the contrasts matrix
• Fit the linear model using the custom contrasts matrix

---

Latest Changes: 2023-04-02 12:56:01 (pvr)