Supervised learning: regression in R

Introduction

In this practical, you will learn how to perform regression analysis in R, how to create predictions, how to plot with confidence and prediction intervals, how to calculate MSE, perform train-test splits, and write a function for cross validation.

Just like in the practical at the end of chapter 3 of the ISLR book, we will use the Boston dataset, which is in the MASS package that comes with R.

library(ISLR)
library(MASS)
library(tidyverse)

Make sure to load MASS before tidyverse otherwise the function MASS::select() will overwrite dplyr::select()

Regression in R

Regression is performed through the lm() function. It requires two arguments: a formula and data. A formula is a specific type of object that can be constructed like so:

some_formula <- outcome ~ predictor_1 + predictor_2 

You can read it as “the outcome variable is a function of predictors 1 and 2”. As with other objects, you can check its class and even convert it to other classes, such as a character vector:

class(some_formula)
as.character(some_formula)

You can estimate a linear model using lm() by specifying the outcome variable and the predictors in a formula and by inputting the dataset these variables should be taken from.

1. Create a linear model object called lm_ses using the formula medv ~ lstat and the Boston dataset.

You have now trained a regression model with medv (housing value) as the outcome/dependent variable and lstat (socio-economic status) as the predictor / independent variable.

Remember that a regression estimates \(\beta_0\) (the intercept) and \(\beta_1\) (the slope) in the following equation:

\[\boldsymbol{y} = \beta_0 + \beta_1\cdot \boldsymbol{x}_1 + \boldsymbol{\epsilon}\]

2. Use the function coef() to extract the intercept and slope from the lm_ses object. Interpret the slope coefficient.

3. Use summary() to get a summary of the lm_ses object. What do you see? You can use the help file ?summary.lm.

We now have a model object lm_ses that represents the formula

\[\text{medv}_i = 34.55 - 0.95 * \text{lstat}_i + \epsilon_i\]

With this object, we can predict a new medv value by inputting its lstat value. The predict() method enables us to do this for the lstat values in the original dataset.

4. Save the predicted y values to a variable called y_pred

5. Create a scatter plot with y_pred mapped to the x position and the true y value (Boston$medv) mapped to the y value. What do you see? What would this plot look like if the fit were perfect?

We can also generate predictions from new data using the newdata argument in the predict() method. For that, we need to prepare a data frame with new values for the original predictors.

6. Use the seq() function to generate a sequence of 1000 equally spaced values from 0 to 40. Store this vector in a data frame with (data.frame() or tibble()) as its column name lstat. Name the data frame pred_dat.

7. Use the newly created data frame as the newdata argument to a predict() call for lm_ses. Store it in a variable named y_pred_new.

Plotting lm() in ggplot

A good way of understanding your model is by visualizing it. We are going to walk through the construction of a plot with a fit line and prediction / confidence intervals from an lm object.

8. Create a scatter plot from the Boston dataset with lstat mapped to the x position and medv mapped to the y position. Store the plot in an object called p_scatter.

Now we’re going to add a prediction line to this plot.

9. Add the vector y_pred_new to the pred_dat data frame with the name medv.

10. Add a geom_line() to p_scatter, with pred_dat as the data argument. What does this line represent?

11. The interval argument can be used to generate confidence or prediction intervals. Create a new object called y_pred_95 using predict() (again with the pred_dat data) with the interval argument set to “confidence”. What is in this object?

12. Create a data frame with 4 columns: lstat, medv, lower, and upper using the pred_dat data and y_pred_95.

13. Add a geom_ribbon() to the plot with the data frame you just made. The ribbon geom requires three aesthetics: x (lstat, already mapped), ymin (lower), and ymax (upper).

Add the ribbon first, then the geom_line() and the geom_points() from before to make sure they remain visible. Give it a nice colour and clean up the plot, too!

14. Explain in your own words what the ribbon represents.

15. Do the same thing, but now with the prediction interval instead of the confidence interval.

Mean squared error

16. Write a function called mse() that takes in two vectors: true y values and predicted y values, and which outputs the mean squared error.

Start like so:

mse <- function(y_true, y_pred) {
  # your function here
}

Wikipedia may help for the formula.

17. Make sure your mse() function works correctly by running the following code.

mse(1:10, 10:1)

You have now calculated the mean squared length of the dashed lines below.

18. Calculate the mean squared error of the lm_ses model. Use the medv column as y_true and use the predict() method to generate y_pred.

You have calculated the mean squared length of the dashed lines in the plot below.

Train-validation-test split

Now we will use the sample() function to randomly select observations from the Boston dataset to go into a training, test, and validation set. The training set will be used to fit our model, the validation set will be used to calculate the out-of sample prediction error during model building (this can also be called hyperparameter tuning), and the test set will be used to estimate the true out-of-sample MSE.

19. The Boston dataset has 506 observations. Use c() and rep() to create a vector with 253 times the word “train”, 152 times the word “validation”, and 101 times the word “test”. Call this vector splits.

20. Use the function sample() to randomly order this vector and add it to the Boston dataset using mutate(). Assign the newly created dataset to a variable called boston_master.

21. Now use filter() to create a training, validation, and test set from the boston_master data. Call these datasets boston_train, boston_valid, and boston_test.

We will set aside the boston_test dataset for now.

22. Train a linear regression model called model_1 using the training dataset. Use the formula medv ~ lstat like in the first lm() exercise. Use summary() to check that this object is as you expect.

23. Calculate the MSE with this object. Save this value as model_1_mse_train.

24. Now calculate the MSE on the validation set and assign it to variable model_1_mse_valid. Hint: use the newdata argument in predict().

This is the estimated out-of-sample mean squared error.

25. Create a second model model_2 for the train data which includes age and tax as predictors. Calculate the train and validation MSE.

26. Compare model 1 and model 2 in terms of their training and validation MSE. Which would you choose and why?

27. Calculate the test MSE for the model of your choice in the previous question. What does this number tell you?

OPTIONAL Programming exercise: cross-validation

This is an advanced exercise. Some components we have seen before in this and previous practicals, but some things will be completely new. Try to complete it by yourself, but don’t worry if you get stuck. If you don’t know about for loops in R, read up on those before you start the exercise.

Use help in this order:

• R help files
• Internet search & stack exchange
• Your peers
• The answer, which shows one solution

You may also just read the answer and try to understand what happens in each step.

28. Create a function that performs k-fold cross-validation for linear models.

Inputs:

• formula: a formula just as in the lm() function
• dataset: a data frame
• k: the number of folds for cross validation
• any other arguments you need

Outputs:

• Mean squared error averaged over folds

29. Use your function to perform 9-fold cross validation with a linear model with as its formula medv ~ lstat + age + tax. Compare it to a model with as formula medv ~ lstat + I(lstat^2) + age + tax.