从R的Logistic回归解释系数 [英] Interpreting coefficients from Logistic Regression from R

问题描述

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我对一组分类变量和连续变量进行了逻辑回归，并以二进制事件作为因变量.

I ran a logistic Regression on a set of variables both categorical and continuous with a binary event as dependent variable.

现在，在建模之后，我观察到一组显示负号的分类变量，我认为这是为了理解，如果该分类变量出现的次数很高，那么因变量出现的可能性就很低.

Now post modelling, I observe a set of categorical variables showing negative sign which I presume is to understand that if that categorical variable occurs high number of times then the probability of the dependent variable occurring is low.

但是，当我看到该独立变量的发生百分比时，我看到了反向趋势的发生.因此结果似乎是反直觉的.发生这种情况的任何原因.我尝试在下面用一个伪示例进行解释.

But when I see the % of occurrence of that independent variable I see the reverse trend happening. hence the result seems to be counter intuitive. Any reason why this could happen. I have tried explaining below with a pseudo example.

因变量-E预测变量:1.分类变量-具有1个级别(0,1)的Cat12.连续变量-Con13.类别变量-具有2级(0,1)的Cat2后期建模:说一切都很重要，系数如下所示，类别1-(-0.6)Con1-(0.3)Cat2-(-0.4)

Dependent Variable - E Predictors: 1. Categorical Var - Cat1 with 2 levels (0,1) 2. Continuous Var - Con1 3. Categorical Var - Cat2 with 2 levels (0,1) Post Modelling: Say all are significant and the coefficients are like below, Cat1 - (-0.6) Con1- (0.3) Cat2 - (-0.4)

但是，当我计算Cat 1上事件E的发生百分比时，我发现当Cat1为1时，发生百分比很高，我认为这与直觉相反.

But when I calculate the % of occurrence of Event E on Cat 1, I observe that the % of occurence is high when Cat1 is 1, which I think is counter intuitive.

请帮助理解这一点.

推荐答案

逻辑回归系数与事件发生概率的变化不直接相关，而是相对于单数变化的相对度量的事件.这篇文章详细介绍了如何解释逻辑回归系数.在您的上下文中，CAT1的系数为-0.6，意味着p(E | CAT1 = 1)<p(E | CAT1 = 0)，与p(E | CAT1 = 1)的大小无关.

Coefficients of logistic regression are not directly related to the chage of probability of the event, rather it's a relative measure of the change in the odds of the event. This article has detailed derivation of how to interpret the coefficients of logistic regression. In your context, the coefficient for CAT1 is -0.6 means p(E|CAT1 = 1) < p(E|CAT1 = 0) and it's not related to exactly how big p(E|CAT1 = 1) is.

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