When working with Julia, it is common to encounter situations where we need to plot residuals on a fitted model. This can be useful for visualizing the accuracy of our model and identifying any patterns or trends in the residuals. In this article, we will explore three different ways to plot residuals on a fitted model in Julia.
Option 1: Using the Gadfly package
The Gadfly package is a powerful plotting library in Julia that provides a high-level interface for creating beautiful and informative visualizations. To plot residuals on a fitted model using Gadfly, we can follow these steps:
using Gadfly
# Fit the model
model = fit(LinearModel, X, y)
# Calculate the residuals
residuals = y - predict(model, X)
# Create a scatter plot of residuals
plot(x = X, y = residuals, Geom.point)
This code snippet first fits a linear model to the data using the `fit` function. Then, it calculates the residuals by subtracting the predicted values from the actual values. Finally, it creates a scatter plot of the residuals using the `plot` function with the `Geom.point` argument.
Option 2: Using the Plots package
The Plots package is another popular plotting library in Julia that provides a unified interface for creating various types of plots. To plot residuals on a fitted model using Plots, we can follow these steps:
using Plots
# Fit the model
model = fit(LinearModel, X, y)
# Calculate the residuals
residuals = y - predict(model, X)
# Create a scatter plot of residuals
scatter(X, residuals)
This code snippet is similar to the previous one, but it uses the `scatter` function from the Plots package to create a scatter plot of the residuals. The `scatter` function automatically infers the type of plot based on the input data.
Option 3: Using the PyPlot package
The PyPlot package is a Julia interface to the popular Matplotlib library in Python. It provides a wide range of plotting capabilities and is especially useful for creating complex and customized plots. To plot residuals on a fitted model using PyPlot, we can follow these steps:
using PyPlot
# Fit the model
model = fit(LinearModel, X, y)
# Calculate the residuals
residuals = y - predict(model, X)
# Create a scatter plot of residuals
scatter(X, residuals)
This code snippet is similar to the previous one, but it uses the `scatter` function from the PyPlot package to create a scatter plot of the residuals. The PyPlot package provides a wide range of customization options for creating publication-quality plots.
After exploring these three options, it is clear that the choice of plotting package depends on the specific requirements of the analysis. If simplicity and ease of use are the main priorities, the Gadfly package is a great choice. However, if more advanced customization options are needed, the Plots or PyPlot packages provide more flexibility. Ultimately, the best option is the one that best suits the needs of the analysis.
