easystats: Quickly investigate model performance

Written by Matt Dancho

Easystats performance is an R package that makes it easy to investigate the relevant assumptions for regression models. I’m super impressed with it! In the next 10-minutes, we’ll:

  • Learn how to investigate performance with performance::check_model()
  • Check out the Tidymodels integration with check_model()
  • [BIG BONUS] Step through each of the 6 Model Performance Plots so you know how to use them.

This article was last updated on: February 11th, 2022.

R-Tips Weekly

This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks.

Here are the links to get set up. 👇

Video Tutorial

Learn how to use easystats’s performance package in our 7-minute YouTube video tutorial.

What you make in this R-Tip

By the end of this tutorial, you’ll make a helpful plot for analyzing the model quality of regression models.

Model Performance Plot (made with easystats)

Thank You Developers.

Before we move on, please recognize that the easystats-verse of R packages is developed by Daniel Lüdecke, Dominique Makowski, Mattan S. Ben-Shachar, Indrajeet Patil, and Brenton M. Wiernik. Thank you for all that you do!

Model performance tutorial

Let’s get up and running with the performance package so we can assess Model Performance.

Step 1: Load the Libraries and Data

First, run this code to:

  1. Load Libraries: Load performance and tidyverse.
  2. Import Data: We’re using the mpg dataset that comes with ggplot2.

Get the code.

Load the data. We’re using the mpg dataset.

Step 2: Make & Check a Linear Regression Model

Next, we’ll quickly make a Linear Regression model with tidymodels. Then I’ll cover more specifics on what we are doing. Here’s the code. We follow 3-Steps:

  1. Load Tidymodels: This loads parsnip (the modeling package in the tidymodels ecosystem)
  2. Make Linear Regression Model: We set up a model specification using linear_reg().
    • We then select an engine with set_engine(). In our case we want “lm”, which connects to stats::lm().
    • We then fit() the model. We use a formula hwy ~ displ + class to make highway fuel economy our target and displacement and vehicle class our predictors. This creates a trained model.
  3. Run Check Model: With a fitted model in hand, we can run performance::check_model(), which generates the Model Performance Plot.

Get the code.

Step 3: Check out the 6-Model Performance Plots!

Here is the output of check_model(), which returns a Model Performance Plot. This is actually 6-plots in one. We’ll go through them next.

Model Performance Plot

Let’s go through the plots, analyzing our model performance.

BONUS: Analyzing the 6 Model Performance Plots

Let’s step through the 6-plots that were returned.

Plots 1 & 2: Residual Linearity

The first two plots analyze the linearity of the residuals (in-sample model error) versus the fitted values. We want to make sure that our model is error is relatively flat.

Residual Linearity

Quick Assessment:

  • We can see that when our model predictions are around 30, our model has larger error compared to below 30. We may want to inspect these points to see what could be contributing to the lower predictions than actuals.

Plots 3 & 4: Collinearity and High Leverage Points

The next two plots analyze for collinearity and high leverage points.

  • Collinearity is when features are highly correlated, which can throw off simple regression models (more advanced models use a concept called regularization and hyperparameter tuning to control for collinearity).
  • High Leverage Points are observations that deviate far from the average. These can skew the predictions for linear models, and removal or model adjustment may be necessary to control model performance.

Collinearity and High Leverage Points

Quick Assessment:

  • Collinearity: We can see that both of the features have low collinearity (green bars). No model adjustments are necessary.
  • Influential Observations: None of the predictins are outside of the contour lines indicating we don’t have high leverage points. No model adjustments are necessary.

Plots 5& 6: Normality of Residuals

The last two plots analyze for the normality of residuals, which is how the model error is distributed. If the distributions are skewed, this can indicate problems with the model.

Residual Normality

Quick Assessment:

  • Quantile-Quantile Plot: We can see that several points towards the end of the quantile plot do fall along the straight-line. This indicates that the model is not predicting well for these points. Further inspection is required.
  • Normal Density Plot: We can see there is a slight increase in density around 15, which looks to shift the distribution to the left of zero. This means that the high-error predictions should be investigated further to see why the model is far off on this subset of the data.


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