RegressionVisualiser3D is a simple single-function plotting toolbox for visualizing linear regression, Poisson regression and binary logistic regression in 3D, for teaching purposes. It is designed to be used in a Jupyter Notebook, but can also be used from the terminal (set markdown = False if using from the terminal).
A notebook demonstrating the package is here: https://nbviewer.org/github/pxr687/RegressionVisualiser3D/blob/master/RegressionVisualiser3D_Demo.ipynb
The user supplies parameters for the data-generating process and a population of observations is created through those parameters. The population data consists of two continuous predictor variables and one outcome variable. The type of outcome variable depends on the type of regression model being visualized (continuous for linear regression, binary for logistic regression etc.)..
A random sample (of a size specified by the user) is drawn from the population data. A regression model is then fit to the sample data.
3D visualisations are then shown, which depict:
- The population data
- The population regression surface
- The sample data
- The sample regression surface
An optional regression table (with slopes and p-values etc.) is also shown, alongside the true regression equation used to generate the data.
To aid understanding, the user can also specify the names of the predictor variables and the outcome variable.
If the user does not supply population parameters, defaults are used. (The script
generate_legal_params.py in the top level of the repo can be used to generate
new legal defaults parameters, for the default axis dimensions... Legal default
parameters are stored in .csv files in the legal_default parameters folder.)
Important Note: Certain combinations of parameters will generate computational errors. In that instance play around with them until you find combinations which do not cause errors. To use for teaching, it's best to find legal combinations ahead of time :)