Tidy Model Results: Exercises

Common problems with complex modeling analysis with R is that model results are often complex objects and getting to values, like model coefficients, demand a lot of manipulations; others vary from one model to another. Fortunately, the broom package provides nice and easy to use solutions to the problem.

Answers to the exercises are available here.

Please do all exercises using the tidiverse packages (mostly broom and ggplot2.) If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

Exercise 1

The plot shows displacement vs. miles per gallon for the mtcars data set. Fit the data with the linear model and display the model’s coefficients as a tidy data.frame.

Exercise 2
Reconstruct the plot from exercise 1, adding a prediction line.

Exercise 3
Fit the data with inverse relations instead of a linear one. Like before, display the coefficients and add a prediction line to the plot. (Hint: use nls function)

Exercise 4
Compare the performance of both models.

Exercise 5
To evaluate the model stability, re-fit the better of the models 100 times with the bootstrap procedure and plot histograms of coefficients.

Exercise 6
Plot the predictions from bootstrapped models.

Exercise 7
Fit the model once again, but this time, separately for a different number of cylinders (cyl variable). Plot the prediction for each group separately, plus the prediction from the previous model.

Learn more about evaluating different statistical models in the online courses Linear regression in R for Data Scientists and Structural equation modeling (SEM) with lavaan. These courses cover different statistical models that can help you choose the right design for your solution.

Exercise 8
Display the model performance summaries. Compare the sum of deviance with a model not grouped by the number of cylinders.

Exercise 9
Apply k-means on the iris data set testing k from 2 to 5. Display the performance summary for each number of clusters.

Exercise 10
Plot the Petal.Length vs. Petal.Width showing assignment to clusters and centers of the cluster.

Related exercise sets:

Linear Regression in Tensorflow Evaluate your model with R Exercises Generalized linear functions (Beginners) How to prepare and apply machine learning to your dataset Advanced Techniques With Raster Data – Part 3: Exercises