https://github.com/calcuis/path-analysis

an example of a Python function that performs a path analysis
https://github.com/calcuis/path-analysis

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an example of a Python function that performs a path analysis

• Host: GitHub
• URL: https://github.com/calcuis/path-analysis
• Owner: calcuis
• Created: 2022-12-08T05:43:25.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-08T05:44:41.000Z (over 2 years ago)
• Last Synced: 2025-01-21T13:11:57.297Z (5 months ago)
• Language: Python
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# path-analysis

an example of a Python function that performs a path analysis

```

import numpy as np

from sklearn.linear_model import LinearRegression

def perform_path_analysis(data, paths):

    # Create the LinearRegression object

    regressor = LinearRegression()

    # Initialize the output dictionary

    results = {}

    # Loop over the paths

    for path in paths:

        # Extract the predictor and outcome variables for the current path

        predictor = data[path[0]]

        outcome = data[path[1]]

        # Fit the predictor and outcome variables to the regressor

        regressor.fit(predictor, outcome)

        # Get the coefficients and store them in the results dictionary

        results[path] = regressor.coef_

    # Return the results

    return results

```

In this function, we first import the necessary modules. Then, we define the function `perform_path_analysis`, which takes in two arguments: the data to be analyzed, and a list of paths to be examined.

Next, we create a `LinearRegression` object, which we will use to fit the data and calculate the coefficients for each path. We also initialize an empty dictionary to store the results.

Then, we loop over the paths and extract the predictor and outcome variables for each path. We use the `LinearRegression` object to fit the predictor and outcome variables, and then store the coefficients in the results dictionary.

Finally, we return the results dictionary, which contains the coefficients for each path. These coefficients can be used to interpret the relationships between the variables in the data and understand the underlying structure of the data.