Metadata-Version: 2.1
Name: pybetareg
Version: 1.0.0
Summary: Beta modal regression
Home-page: https://rh8liuqy.github.io/
Author: Qingyang Liu
Author-email: qingyang@email.sc.edu
License: MIT
Description: # Beta modal regression with measurement error
        
        ## Import data
        
            import numpy as np
            import pandas as pd
            import matplotlib.pyplot as plt
            import pybetareg as pyb
        
            ## Beta Modal Regression in Python.
        
            df1 = pd.read_csv("data.csv")
            df1.head()
        
            ##           Y      Wbar    SigmaW   Z1
            ## 0  0.186046 -2.289838  1.732051  0.0
            ## 1  0.391666 -0.535476  1.732051  0.0
            ## 2  0.883178  2.071954  1.732051  1.0
            ## 3  0.727209 -0.578447  1.732051  0.0
            ## 4  0.269854 -0.926259  1.732051  0.0
        
            y = df1['Y'].to_numpy()
            w = df1['Wbar'].to_numpy()
            z = df1['Z1'].to_numpy()
            z = np.column_stack([np.ones(z.shape[0]),z])
            sigmaw = df1['SigmaW'].to_numpy()
        
        ## Fit model
        
            model2 = pyb.reg_measurement_error(y=y,w=w,z=z,
                                               sigmaw=sigmaw,
                                               initial=[10,1,1,1],
                                               CUDA = True,
                                               column_names = ['b1','b0','b2'])
            model2fit = model2.fit()
            model2fit.summary()
        
            ## -----------------------Model fitting completes------------------------
            ## Success:True
            ## Optimization terminated successfully.
            ## """
            ##                   Beta Modal Regression Results With                  
            ##                      Measurement Error Adjustment                     
            ## ======================================================================
            ##                 coef   std err         z     P>|z|    [0.025    0.975]
            ## ----------------------------------------------------------------------
            ## m            12.3424     3.791     3.256     0.001     4.913    19.772
            ## b1            0.9733     0.453     2.150     0.032     0.086     1.860
            ## b0            1.0646     0.436     2.444     0.015     0.211     1.918
            ## b2            0.9807     0.442     2.217     0.027     0.114     1.847
            ## ======================================================================
            ## """
        
        ## Hotelling's *T*<sup>2</sup> statistic and parametric bootstrap *p*-value.
        
        Use `hotelling_p(50)` function to calculate Hotelling's *T*<sup>2</sup>
        statistic and parametric bootstrap *p*-value across 50 iterations.
        
            model2.hotelling_p(50)
        
            ## Hotelling's T^2 statistic and parametric bootstrap p-value.      
            ## ======================================================================
            ## Hotelling's T^2 statistic: 0.5063
            ## parametric bootstrap p-value: 0.7000
            ## ======================================================================
        
        # Beta modal regression without measurement error
        
        ## Import data
        
            df2 = pd.read_csv("data2.csv")
            df2.head()
        
            ##           Y   X0        X1   X2
            ## 0  0.133439  1.0 -2.223525  0.0
            ## 1  0.315374  1.0 -1.415762  0.0
            ## 2  0.845555  1.0  1.218485  1.0
            ## 3  0.977328  1.0  1.690799  1.0
            ## 4  0.811748  1.0  0.076872  0.0
        
        ## Fit model
        
            x = df2[['X0','X1','X2']]
            y = df2['Y']
            model1 = pyb.reg(x=x, y=y, initial = [10,1,1,1])
            model1fit = model1.fit()
            model1fit.summary()
        
            ## Link function:logit
            ## Columns names are not given.
            ## Success:True
            ## Optimization terminated successfully.
            ## """
            ##                     Beta Modal Regression Results                     
            ## ======================================================================
            ##                 coef   std err         z     P>|z|    [0.025    0.975]
            ## ----------------------------------------------------------------------
            ## m            11.1426     1.253     8.891     0.000     8.686    13.599
            ## beta0         0.9453     0.113     8.373     0.000     0.724     1.167
            ## beta1         0.8837     0.084    10.571     0.000     0.720     1.048
            ## beta2         1.1198     0.182     6.158     0.000     0.763     1.476
            ## ======================================================================
            """
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
