https://github.com/calcuis/rasch-analysis
an example of a Python function that performs Rasch analysis
https://github.com/calcuis/rasch-analysis
Last synced: 3 months ago
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an example of a Python function that performs Rasch analysis
- Host: GitHub
- URL: https://github.com/calcuis/rasch-analysis
- Owner: calcuis
- Created: 2022-12-08T05:52:52.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-08T05:54:15.000Z (over 2 years ago)
- Last Synced: 2025-01-21T13:11:56.282Z (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
# rasch-analysis
an example of a Python function that performs Rasch analysis
```
import numpy as np
from sklearn.linear_model import LinearRegression
def perform_rasch_analysis(data):
# Create the LinearRegression object
regressor = LinearRegression()
# Extract the predictors and outcomes from the data
X = data[:, :-1]
y = data[:, -1]
# Fit the predictors and outcomes to the regressor
regressor.fit(X, y)
# Get the coefficients and store them in the results dictionary
results = {"coefficients": regressor.coef_}
# Return the results
return results
```
In this function, we first import the necessary modules. Then, we define the function `perform_rasch_analysis`, which takes in a single argument: the data to be analyzed.
Next, we create a `LinearRegression` object, which we will use to fit the data and calculate the coefficients. We also extract the predictors and outcomes from the data.
Then, we use the `LinearRegression` object to fit the predictors and outcomes, and store the coefficients in a results dictionary.
Finally, we return the results dictionary, which contains the coefficients calculated by the Rasch analysis. These coefficients can be used to interpret the relationships between the variables in the data and understand the underlying structure of the data.