[英]How to calculate the 99% confidence interval for the slope in a linear regression model in python?
We have following linear regression: y ~ b0 + b1 * x1 + b2 * x2. 我们有以下线性回归:y~b0 + b1 * x1 + b2 * x2。 I know that regress function in Matlab does calculate it, but numpy's linalg.lstsq doesn't ( https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html ).
我知道Matlab中的回归函数确实计算了它,但是numpy的linalg.lstsq没有( https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html )。
StatsModels' RegressionResults
has a conf_int()
method. StatsModels的
RegressionResults
有一个conf_int()
方法。 Here an example using it (minimally modified version of their Ordinary Least Squares example): 这是一个使用它的例子( 普通最小二乘示例的最小修改版本):
import numpy as np, statsmodels.api as sm
nsample = 100
x = np.linspace(0, 10, nsample)
X = np.column_stack((x, x**2))
beta = np.array([1, 0.1, 10])
e = np.random.normal(size=nsample)
X = sm.add_constant(X)
y = np.dot(X, beta) + e
mod = sm.OLS(y, X)
res = mod.fit()
print res.conf_int(0.01) # 99% confidence interval
You can use scipy's linear regression, which does calculate the r/p value and standard error : http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.linregress.html 您可以使用scipy的线性回归,它可以计算r / p值和标准误差: http : //docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.linregress.html
EDIT : as underlines by Brian, I had the code from scipy documentation: 编辑:由Brian强调,我从scipy文档获得了代码:
from scipy import stats
import numpy as np
x = np.random.random(10)
y = np.random.random(10)
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
confidence_interval = 2.58*std_err
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