Python uses Statsmodes for Kernel density estimation, Quantile regression, regression tree, etc
To use Statsmodes in Python for Kernel density estimation, Quantile regression and regression tree analysis, you need to ensure that Statsmodes and related class libraries have been installed. The following steps can be used to prepare for environment setup:
1. Install Statsmodes library:
pip install statsmodels
2. Install dependent class libraries:
-NumPy: Used to handle numerical calculations.
-Pandas: used for data processing and analysis.
-SciPy: used for scientific calculations and statistics.
-Matplotlib: used for drawing and Data and information visualization.
You can install these libraries using the following command:
pip install numpy pandas scipy matplotlib
Next, we will introduce examples of Kernel density estimation, Quantile regression and regression tree using Statsmodes, and provide a dataset for experiments.
**Dataset introduction:**
To demonstrate Kernel density estimation and Quantile regression, we will use the 'star98' data set that comes with Statsmodes. This dataset is a virtual dataset that contains various variables used to predict school comprehensive scores.
**Dataset download website:**
You can download the 'star98' dataset through the following code:
python
import statsmodels.api as sm
data = sm.datasets.get_rdataset('star98').data
**Sample code:**
The following is a complete sample code including Kernel density estimation, Quantile regression and regression tree:
python
import statsmodels.api as sm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
#Import the star98 dataset
data = sm.datasets.get_rdataset('star98').data
#Kernel density estimation
density = sm.nonparametric.KDEUnivariate(data['pctymle'])
density.fit()
#Draw Kernel density estimation chart
plt.plot(density.support, density.density)
plt.xlabel('Percentage of young male with low earnings')
plt.ylabel('Density')
plt.show()
#Quantile regression
quantiles = np.arange(0.1, 1, 0.1)
mod = sm.quantreg('np.log(r1) ~ np.log(pctymle)', data)
res = mod.fit(q=quantiles)
#Output regression results
print(res.summary())
#Regression tree
from sklearn.tree import DecisionTreeRegressor
#Prepare data
X = data[['pctymle', 'st_ratio']]
y = data['r1']
#Fitting regression Tree model
model = DecisionTreeRegressor(max_depth=2)
model.fit(X, y)
#Draw Regression Tree
from sklearn import tree
plt.figure(figsize=[10, 5])
_ = tree.plot_tree(model, filled=True, feature_names=['pctymle', 'st_ratio'])
plt.show()
The above code implements examples of Kernel density estimation, Quantile regression and regression tree using Statsmodes. You can modify the dataset and related parameters according to your own needs.