[英]Evaluating a t-test on regression coefficients using statsmodels
I have a dataset with about 100+ features. 我有约100多个要素的数据集。 I also have a small set of covariates.
我也有一小部分协变量。
I build an OLS linear model using statsmodels for y = x + C1 + C2 + C3 + C4 + ... + Cn for each covariate, and a feature x, and a dependent variable y. 我为每个协变量使用statsmodels建立y = x + C1 + C2 + C3 + C4 + ... + Cn的OLS线性模型,并使用特征x和因变量y。
I'm trying to perform hypothesis testing on the regression coefficients to test if the coefficients are equal to 0. I figured a t-test would be the appropriate approach to this, but I'm not quite sure how to go about implementing this in Python, using statsmodels. 我正在尝试对回归系数进行假设检验,以检验系数是否等于0。我认为t检验将是解决此问题的适当方法,但是我不确定如何在Python,使用statsmodels。
I know, particularly, that I'd want to use http://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.RegressionResults.t_test.html#statsmodels.regression.linear_model.RegressionResults.t_test 我特别知道,我想使用http://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.RegressionResults.t_test.html#statsmodels.regression.linear_model.RegressionResults.t_test
But I am not certain I understand the r_matrix parameter. 但是我不确定我是否了解r_matrix参数。 What could I provide to this?
我能为此提供什么? I did look at the examples but it is unclear to me.
我确实看过这些例子,但我不清楚。
Furthermore, I am not interested in doing the t-tests on the covariates themselves, but just the regression co-eff of x. 此外,我对协变量本身的t检验不感兴趣,而只对x的回归系数感兴趣。
Any help appreciated! 任何帮助表示赞赏!
Are you sure you don't want statsmodels.regression.linear_model.OLS
? 您确定不希望
statsmodels.regression.linear_model.OLS
吗? This will perform a OLS regression, making available the parameter estimates and the corresponding p-values (and many other things). 这将执行OLS回归,从而提供参数估计值和相应的p值(以及许多其他内容)。
from statsmodels.regression import linear_model
from statsmodels.api import add_constant
Y = [1,2,3,5,6,7,9]
X = add_constant(range(len(Y)))
model = linear_model.OLS(Y, X)
results = model.fit()
print(results.params) # [ 0.75 1.32142857]
print(results.pvalues) # [ 2.00489220e-02 4.16826428e-06]
These p-values are from the t-tests of each fit parameter being equal to 0. 这些p值来自每个拟合参数等于0的t检验。
It seems like RegressionResults.t_test
would be useful for less conventional hypotheses. 看起来
RegressionResults.t_test
对于不太传统的假设很有用。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.