我查看了文檔，但仍然無法弄清楚。我想運行具有多重回歸的 WLS。statsmodels.api 作為 sm 導(dǎo)入單變量示例。X = Height
Y = Weight
res = sm.OLS(Y,X,).fit()
res.summary()說我也有：X2 = 年齡如何將其添加到我的回歸中？
2 回答
飲歌長嘯
TA貢獻1951條經(jīng)驗 獲得超3個贊
您可以將它們放入 data.frame 并調(diào)出列（這樣輸出看起來也更好）：
import statsmodels.api as sm
import pandas as pd
import numpy as np
Height = np.random.uniform(0,1,100)
Weight = np.random.uniform(0,1,100)
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
==============================================================================
POPMUISE
TA貢獻1765條經(jīng)驗 獲得超5個贊
我使用二階多項式來預(yù)測身高和年齡如何影響士兵的體重。您可以在我的 GitHub 上獲取 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(60,100,50)
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')
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