Question

我有一个像

这样的列的Pandas数据框

Order     Balance     Profit cum (%)

我正在进行线性回归

model_profit_tr = pd.ols(y=df_closed['Profit cum (%)'], x=df_closed['Order'])

这个问题是标准模型就像（不通过原点的线的方程）

y = a * x + b

有2个自由度（a和b）

斜率（a）：

a=model_profit_tr.beta['x']

和拦截（b）：

b=model_profit_tr.beta['intercept']

我想降低我的模型的自由度（从2到1），我希望有一个像

这样的模型

y = a * x

Answer 1

使用intercept关键字参数：

model_profit_tr = pd.ols(y=df_closed['Profit cum (%)'], 
                         x=df_closed['Order'], 
                         intercept=False)

来自docs：

In [65]: help(pandas.ols) 
Help on function ols in module pandas.stats.interface:

ols(**kwargs)

    [snip]

    Parameters
    ----------
    y: Series or DataFrame
        See above for types
    x: Series, DataFrame, dict of Series, dict of DataFrame, Panel
    weights : Series or ndarray
        The weights are presumed to be (proportional to) the inverse of the
        variance of the observations.  That is, if the variables are to be
        transformed by 1/sqrt(W) you must supply weights = 1/W
    intercept: bool
        True if you want an intercept.  Defaults to True.
    nw_lags: None or int
        Number of Newey-West lags.  Defaults to None.

    [snip]

Answer 2

以下是显示解决方案的示例：

#!/usr/bin/env python

import pandas as pd
import matplotlib.pylab as plt
import numpy as np

data = [
(0.2, 1.3),
(1.3, 3.9),
(2.1, 4.8),
(2.9,5.5),
(3.3,6.9)
]

df = pd.DataFrame(data, columns=['X', 'Y'])

print(df)

# 2 degrees of freedom : slope / intercept
model_with_intercept = pd.ols(y=df['Y'], x=df['X'], intercept=True)
df['Y_fit_with_intercept'] = model_with_intercept.y_fitted

# 1 degree of freedom : slope ; intersept=0
model_no_intercept = pd.ols(y=df['Y'], x=df['X'], intercept=False)
df['Y_fit_no_intercept'] = model_no_intercept.y_fitted

# 1 degree of freedom : slope ; intersept=offset
offset = -1
df['Yoffset'] = df['Y'] - offset
model_with_offset = pd.ols(y=df['Yoffset'], x=df['X'], intercept=False)
df['Y_fit_offset'] = model_with_offset.y_fitted + offset

print(model_with_intercept)
print(model_no_intercept)
print(model_with_offset)

df.plot(x='X', y=['Y', 'Y_fit_with_intercept', 'Y_fit_no_intercept', 'Y_fit_offset'])
plt.show()

线性回归 - 减少自由度

2 个答案: