如何解决多变量回归statsmodels.api
我已经浏览了文档，但仍然无法弄清楚。我想运行具有多个回归的WLS。
statsmodels.api作为sm导入
单个变量的示例。
X = Height
Y = Weight
res = sm.OLS(Y,X,).fit()
res.summary()
说我也有：
X2 =年龄
如何将其添加到回归中？
解决方法
您可以将它们放入data.frame中并调出列（这样，输出也看起来更好）：
import statsmodels.api as sm
import pandas as pd
import numpy as np
Height = np.random.uniform(0,1,100)
Weight = np.random.uniform(0,100)
Age = np.random.uniform(0,30,100)
df = pd.DataFrame({'Height':Height,'Weight':Weight,'Age':Age})
res = sm.OLS(df['Height'],df[['Weight','Age']]).fit()
In [10]: res.summary()
Out[10]:
<class 'statsmodels.iolib.summary.Summary'>
"""
OLS Regression Results
=======================================================================================
Dep. Variable: Height R-squared (uncentered): 0.700
Model: OLS Adj. R-squared (uncentered): 0.694
Method: Least Squares F-statistic: 114.3
Date: Mon,24 Aug 2020 Prob (F-statistic): 2.43e-26
Time: 15:54:30 Log-Likelihood: -28.374
No. Observations: 100 AIC: 60.75
Df Residuals: 98 BIC: 65.96
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Weight 0.1787 0.090 1.988 0.050 0.000 0.357
Age 0.0229 0.003 8.235 0.000 0.017 0.028
==============================================================================
Omnibus: 2.938 Durbin-Watson: 1.813
Prob(Omnibus): 0.230 Jarque-Bera (JB): 2.223
Skew: -0.211 Prob(JB): 0.329
Kurtosis: 2.404 Cond. No. 49.7
==============================================================================
我使用二阶多项式来预测身高和年龄如何影响士兵的体重。你可以在我的 GitHub 上获取 ansur_2_m.csv。
df=pd.read_csv('ANSUR_2_M.csv',encoding = "ISO-8859-1",usecols=['Weightlbs','Heightin','Age'],dtype={'Weightlbs':np.integer,'Heightin':np.integer,'Age':np.integer})
df=df.dropna()
df.reset_index()
df['Heightin2']=df['Heightin']**2
df['Age2']=df['Age']**2
formula="Weightlbs ~ Heightin+Heightin2+Age+Age2"
model_ols = smf.ols(formula,data=df).fit()
minHeight=df['Heightin'].min()
maxHeight=df['Heightin'].max()
avgAge = df['Age'].median()
print(minHeight,maxHeight,avgAge)
df2=pd.DataFrame()
df2['Heightin']=np.linspace(60,100,50)
df2['Heightin2']=df2['Heightin']**2
df2['Age']=28
df2['Age2']=df['Age']**2
df3=pd.DataFrame()
df3['Heightin']=np.linspace(60,50)
df3['Heightin2']=df2['Heightin']**2
df3['Age']=45
df3['Age2']=df['Age']**2
prediction28=model_ols.predict(df2)
prediction45=model_ols.predict(df3)
plt.clf()
plt.plot(df2['Heightin'],prediction28,label="Age 28")
plt.plot(df3['Heightin'],prediction45,label="Age 45")
plt.ylabel="Weight lbs"
plt.xlabel="Height in"
plt.legend()
plt.show()
print('A 45 year old soldier is more probable to weight more than an 28 year old soldier')
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 dio@foxmail.com 举报，一经查实，本站将立刻删除。
