Getting started

Go to our class’s GitHub organization sta-363-s20
Find the GitHub repository (which we’ll refer to as “repo” going forward) for this lab, lab-05-non-linear-YOUR-GITHUB-HANDLE. This repo contains a template you can build on to complete your assignment.
On GitHub, click on the green Clone or download button, select Use HTTPS. Click on the clipboard icon to copy the repo URL.
If using RStudio Cloud, go to RStudio Cloud and into the course workspace. Create a New Project from Git Repo. You will need to click on the down arrow next to the New Project button to see this option.
If using RStudio Pro, create a new project by clicking File > New Project Then click Version Control and Git/Github.
Copy and paste the URL of your assignment repo into the dialog box.
Hit OK, and you’re good to go!

Packages

In this lab we will work with three packages: ISLR for the data, tidyverse which is a collection of packages for doing data analysis in a “tidy” way and tidymodels for statistial modeling.

Now that the necessary packages are installed, you should be able to Knit your document and see the results.

If you’d like to run your code in the Console as well you’ll also need to load the packages there. To do so, run the following in the console.

library(tidyverse) 
library(tidymodels)
library(ISLR)

Note that the packages are also loaded with the same commands in your R Markdown document.

Housekeeping

Git configuration

⊕Your email address is the address tied to your GitHub account and your name should be first and last name.

Go to the Terminal pane
Type the following two lines of code, replacing the information in the quotation marks with your info:

git config --global user.email "your email"
git config --global user.name "your name"

To confirm that the changes have been implemented, run the following

git config --global user.email
git config --global user.name

Password caching

If you would like your git password cached for a week for this project, type the following in the Terminal:

git config --global credential.helper 'cache --timeout 604800'

Project name:

Currently your project is called Untitled Project. Update the name of your project to be “Lab 05 - Nonlinear models”.

Warm up

Before we introduce the data, let’s warm up with some simple exercises.

YAML:

Open the R Markdown (Rmd) file in your project, change the author name to your name, and knit the document.

Commiting and pushing changes:

Go to the Git pane in your RStudio.
View the Diff and confirm that you are happy with the changes.
Add a commit message like “Update team name” in the Commit message box and hit Commit.
Click on Push. This will prompt a dialogue box where you first need to enter your user name, and then your password.

Data

For this lab, we are using Wage data from the ISLR package.

Exercises

Examine the Wage data set from the ISLR package. What are the variables? How many observations are there?
Create a linear model specification, setting the engine to lm. Call this model specification linear_spec.
Create a recipe using the Wage data from the ISLR package. We want to predict the variable wage from age, health_ins, jobclass, education, and race. Fit age using a natural spline. Use tune() to decide how many degrees of freedom to use for the age variable.
Use tune_grid() to fit the linear model specified in Exercise 2 with the recipe created in Exercise 3 using 10-fold cross validation, similar to Lab 04. Choose the model with the lowest RMSE. How many degrees of freedom were used for the age natural spline for this best model? Report the RMSE for this model as well as the chosen degrees of freedom.

Lab 05 - Nonlinear models

Introduction