Getting error: Shapes not aligned, with statsmodels and simple 2 dimensional linear regression

Question

import numpy as np
import statsmodels.api as sm


list21 = [-0.77, -0.625, -0.264, 0.888, 1.8, 2.411, 2.263, 2.23, 1.981, 2.708]
list23 = [-1.203, -1.264, -1.003, -0.388, -0.154, -0.129, -0.282, -0.017, -0.06, 0.275]

X1 = np.asarray(list21)
Y1 = np.asarray(list23)
    
x = X1.reshape(-1, 1)
y = Y1.reshape(-1, 1)

   
model = sm.OLS(x, y)
fit = model.fit()

y_pred = model.predict(x)

错误读作：

--> 161     y_pred = model.predict(x)

ValueError: shapes (10,1) and (10,1) not aligned: 1 (dim 1) != 499 (dim 0)

过去半小时我一直在用头撞墙，请帮忙。

Answer 1

您将预测分配给了错误的变量。使用：

model = sm.OLS(x, y)
fit = model.fit()
y_pred = fit.predict(x)

或使用

model = sm.OLS(x, y).fit()
y_pred = model.predict(x)

无论哪种情况：将 predict 分配给您在 fit()

中使用的变量

编辑

回答你的问题，为什么这条线经过零：你没有定义截距，你可以用 sm.add_constant 来定义截距。请参考此文档：https://www.statsmodels.org/dev/examples/notebooks/generated/ols.html

应用于您的代码，您将获得：

import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt

list21 = [-0.77, -0.625, -0.264, 0.888, 1.8, 2.411, 2.263, 2.23, 1.981, 2.708]
list23 = [-1.203, -1.264, -1.003, -0.388, -0.154, -0.129, -0.282, -0.017, -0.06, 0.275]

x = np.asarray(list21)
y = np.asarray(list23)
X = sm.add_constant(x)
model = sm.OLS(y,X)
results = model.fit()
y_pred = results.predict(X)
plt.scatter(list21,list23)
plt.plot(x,y_pred)

Getting error: Shapes not aligned, with statsmodels and simple 2 dimensional linear regression

Getting error: Shapes not aligned, with statsmodels and simple 2 dimensional linear regression

python

numpy

statsmodels