Question

数据：https://courses.edx.org/c4x/MITx/15.071x_2/asset/NBA_train.csv

我知道如何使用statsmodels.formula.api将这些数据拟合到多元线性回归模型：

import pandas as pd
NBA = pd.read_csv("NBA_train.csv")
import statsmodels.formula.api as smf
model = smf.ols(formula="W ~ PTS + oppPTS", data=NBA).fit()
model.summary()

然而，我发现这个类似R的公式表示法很尴尬，我想使用通常的pandas语法：

import pandas as pd
NBA = pd.read_csv("NBA_train.csv")    
import statsmodels.api as sm
X = NBA['W']
y = NBA[['PTS', 'oppPTS']]
X = sm.add_constant(X)
model11 = sm.OLS(y, X).fit()
model11.summary()

使用第二种方法我收到以下错误：

ValueError: shapes (835,2) and (835,2) not aligned: 2 (dim 1) != 835 (dim 0)

为什么会发生以及如何解决？

Answer 1

使用sm.OLS(y, X)时，y是因变量，X是。{ 自变量。

在公式W ~ PTS + oppPTS中，W是因变量，PTS和oppPTS是自变量。

因此，请使用

y = NBA['W']
X = NBA[['PTS', 'oppPTS']]

而不是

X = NBA['W']
y = NBA[['PTS', 'oppPTS']]

import pandas as pd
import statsmodels.api as sm

NBA = pd.read_csv("NBA_train.csv")    
y = NBA['W']
X = NBA[['PTS', 'oppPTS']]
X = sm.add_constant(X)
model11 = sm.OLS(y, X).fit()
model11.summary()

产量

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      W   R-squared:                       0.942
Model:                            OLS   Adj. R-squared:                  0.942
Method:                 Least Squares   F-statistic:                     6799.
Date:                Sat, 21 Mar 2015   Prob (F-statistic):               0.00
Time:                        14:58:05   Log-Likelihood:                -2118.0
No. Observations:                 835   AIC:                             4242.
Df Residuals:                     832   BIC:                             4256.
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
const         41.3048      1.610     25.652      0.000        38.144    44.465
PTS            0.0326      0.000    109.600      0.000         0.032     0.033
oppPTS        -0.0326      0.000   -110.951      0.000        -0.033    -0.032
==============================================================================
Omnibus:                        1.026   Durbin-Watson:                   2.238
Prob(Omnibus):                  0.599   Jarque-Bera (JB):                0.984
Skew:                           0.084   Prob(JB):                        0.612
Kurtosis:                       3.009   Cond. No.                     1.80e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.8e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

pandas statsmodels中的多元线性回归：ValueError

1 个答案: