How to get the numerical fitting results when plotting a regression in seaborn

How to get the numerical fitting results when plotting a regression in seaborn

When you plot a regression using Seaborn, you can obtain the numerical fitting results using the scipy.stats module. Seaborn's regplot() function internally uses scipy.stats.linregress() to perform linear regression. Here's how you can plot a regression and obtain the regression coefficients, p-values, and other statistics:

import seaborn as sns import matplotlib.pyplot as plt import numpy as np from scipy import stats # Sample data x = np.array([1, 2, 3, 4, 5]) y = np.array([2, 3.5, 6, 8, 9.5]) # Plot the regression sns.regplot(x=x, y=y) # Calculate linear regression statistics slope, intercept, r_value, p_value, std_err = stats.linregress(x, y) # Print regression results print("Slope:", slope) print("Intercept:", intercept) print("R-squared:", r_value**2) print("P-value:", p_value) print("Standard error:", std_err) # Show the plot plt.xlabel('X') plt.ylabel('Y') plt.title('Regression Plot') plt.show()

In this example:

  1. We use Seaborn's regplot() function to plot the regression line.
  2. We use stats.linregress() from SciPy to perform the linear regression and calculate the fitting results such as slope, intercept, R-squared value, p-value, and standard error.
  3. We print the regression results.

Keep in mind that these calculations assume a linear relationship between the variables. If you're using a different type of regression (e.g., polynomial regression), you'll need to use appropriate methods and functions to obtain the fitting results.

Examples

  1. "Seaborn regression plot with numerical results" Description: Users want to know how to obtain numerical fitting results when plotting a regression using Seaborn.

    # Seaborn regression plot with numerical results import seaborn as sns import matplotlib.pyplot as plt # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables sns.regplot(x='x', y='y', data=data) plt.show()

  2. "How to display regression equation in Seaborn plot?" Description: Users are interested in displaying the regression equation along with the plot generated using Seaborn.

    # How to display regression equation in Seaborn plot import seaborn as sns import matplotlib.pyplot as plt # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables sns.regplot(x='x', y='y', data=data) plt.annotate("y = mx + c", xy=(0.5, 0.5), xycoords='axes fraction') plt.show()

  3. "Seaborn regression plot with slope and intercept" Description: Users want to generate a regression plot using Seaborn with the slope and intercept values displayed.

    # Seaborn regression plot with slope and intercept import seaborn as sns import matplotlib.pyplot as plt # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables sns.regplot(x='x', y='y', data=data) plt.annotate(f"Slope: {slope}, Intercept: {intercept}", xy=(0.5, 0.5), xycoords='axes fraction') plt.show()

  4. "How to get regression coefficients in Seaborn?" Description: This query is about obtaining regression coefficients when plotting a regression using Seaborn.

    # How to get regression coefficients in Seaborn import seaborn as sns # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables result = sns.regplot(x='x', y='y', data=data) slope, intercept = result.get_lines()[0].get_data() print("Slope:", slope) print("Intercept:", intercept)

  5. "Seaborn regression plot with regression coefficients" Description: Users are looking for a way to include regression coefficients in a Seaborn regression plot.

    # Seaborn regression plot with regression coefficients import seaborn as sns import matplotlib.pyplot as plt # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables sns.regplot(x='x', y='y', data=data) plt.text(0.5, 0.5, f"Slope: {slope}, Intercept: {intercept}", horizontalalignment='center', verticalalignment='center') plt.show()

  6. "How to extract regression results from Seaborn plot?" Description: Asking for a method to extract regression results such as coefficients from a Seaborn regression plot.

    # How to extract regression results from Seaborn plot import seaborn as sns # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables result = sns.regplot(x='x', y='y', data=data) slope, intercept = result.get_lines()[0].get_data() print("Slope:", slope) print("Intercept:", intercept)

  7. "Seaborn regression plot with R-squared value" Description: Users want to create a Seaborn regression plot with the R-squared value displayed.

    # Seaborn regression plot with R-squared value import seaborn as sns import matplotlib.pyplot as plt # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables sns.regplot(x='x', y='y', data=data) plt.text(0.5, 0.5, f"R-squared: {r_squared_value}", horizontalalignment='center', verticalalignment='center') plt.show()

  8. "Extract regression equation from Seaborn plot" Description: Seeking a method to extract the regression equation from a Seaborn regression plot.

    # Extract regression equation from Seaborn plot import seaborn as sns # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables result = sns.regplot(x='x', y='y', data=data) slope, intercept = result.get_lines()[0].get_data() print("Regression equation: y =", slope, "* x +", intercept)

  9. "How to calculate regression parameters in Seaborn?" Description: Users are interested in calculating regression parameters such as slope and intercept in Seaborn plots.

    # How to calculate regression parameters in Seaborn import seaborn as sns # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables result = sns.regplot(x='x', y='y', data=data) slope, intercept = result.get_lines()[0].get_data() print("Slope:", slope) print("Intercept:", intercept)

  10. "Seaborn regression plot with p-value" Description: Users want to create a Seaborn regression plot with the p-value displayed.

    # Seaborn regression plot with p-value import seaborn as sns import matplotlib.pyplot as plt # Assume 'data' is your DataFrame and 'x' and 'y' are the column names for x and y variables sns.regplot(x='x', y='y', data=data) plt.text(0.5, 0.5, f"P-value: {p_value}", horizontalalignment='center', verticalalignment='center') plt.show()

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