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How to plot statsmodels linear regression (OLS) cleanly in Matplotlib?
We can plot statsmodels linear regression (OLS) with a non-linear curve but with linear data.
Steps
Set the figure size and adjust the padding between and around the subplots.
To create a new one, we can use seed() method.
Initialize the number of sample and sigma variables.
Create linear data points x, X, beta, t_true, y and res using numpy.
Res is an ordinary Least Square class instance.
Calculate the standard deviation. Confidence interval for prediction applies to WLS and OLS, not to general GLS, that is, independently but not identically distributed observations.
Create a figure and a set of subplots using subplot() method.
Plot all the curves using plot() method with (x, y), (x, y_true), (x, res.fittedvalues), (x, iv_u) and (x, iv_l) data points.
Place the legend on the plot.
To display the figure, use show() method.
Example
import numpy as np from matplotlib import pyplot as plt from statsmodels import api as sm from statsmodels.sandbox.regression.predstd import wls_prediction_std plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True np.random.seed(9876789) nsample = 50 sig = 0.5 x = np.linspace(0, 20, nsample) X = np.column_stack((x, np.sin(x), (x - 5) ** 2, np.ones(nsample))) beta = [0.5, 0.5, -0.02, 5.] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) res = sm.OLS(y, X).fit() prstd, iv_l, iv_u = wls_prediction_std(res) fig, ax = plt.subplots() ax.plot(x, y, 'o', label="data") ax.plot(x, y_true, 'b-', label="True") ax.plot(x, res.fittedvalues, 'r--.', label="OLS") ax.plot(x, iv_u, 'r--') ax.plot(x, iv_l, 'r--') ax.legend(loc='best') plt.show()
Output
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