To run the Fit without bootstrap restarting, set n_boot=0. The number of times this process runs can be controlled with n_boot. For this reason, the Fit method also implements a process called bootstrap restarting which involves taking a bootstrapped resample of the data to try to find a better solution. Muggeo's method doesn't always converge on the best solution - sometimes, it finds a locally optimal solution or doesn't converge at all. See the accompanying paper for more details. This method simultaneously fits breakpoint positions and the linear models for the different fit segments, and it gives confidence intervals for all the model estimates. The package implements Muggeo's iterative algorithm (Muggeo "Estimating regression models with unknown break-points" (2003)) to find breakpoints quickly. You can extract data as well: # Get the key results of the fit Pw_fit.plot_breakpoint_confidence_intervals() Pw_fit.plot_fit(color="red", linewidth=4) # Pass in standard matplotlib keywords to control any of the plots # Plot the data, fit, breakpoints and confidence intervals Optional: Plotting the data and model results: import matplotlib.pyplot as plt NOTE: The Davies hypothesis test for the existence of at least one breakpoint is not reported, and is under development and testing. This includes estimates for all the model variables, along with confidence intervals. Yy = constant + alpha_1*xx + (alpha_2-alpha_1) * np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points)įit the model: # Given some data, fit the model Start-off generating some data with a breakpoint: import piecewise_regression Pw_fit = piecewise_regression.Fit(x, y, n_breakpoints=2)įor demonstration purposes, substitute with your own data to fit. Here is an elementary example, assuming we already have some data x and y: import piecewise_regression You need to specify either a) some initial breakpoint guesses as start_values or b) how many breakpoints you want to fit as n_breakpoints (or both). The package requires some x and y data to fit. The package is tested on Python 3.7, 3.8, 3.9 and 3.10. You can install piecewise-regression using python's pip package index pip install piecewise-regression When using the package, please cite the accompanying paper.Ĭode examples below, and more in this Google Colab Jupyter Notebook. Based on Muggeo's paper "Estimating regression models with unknown break-points" (2003). For fitting straight lines to data where there are one or more changes in gradient (known as breakpoints). Easy-to-use piecewise regression (aka segmented regression) in Python.
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