在回归模型中解释beta估计值时，排序顺序重要吗? [英] Does the sorting order matter when interpreting beta estimates in a regression model?

问题描述

似乎是一个非常基本的问题，但我只是想确认一下.我正在运行针对不同类型的协变量(某些数字，某些类别等)进行调整的多变量线性回归模型.该模型的示例如下所示:

Seems like a very basic question but I just wanted to confirm. I'm running a multivariable linear regression model adjusted for different types of covariates (some numeric, some categorical, etc.). A sample of the model is shown below:

fit <- ols(outcome ~ exposure + age + zbmi + income + sex + ethnicity) 

结果"和结果"和暴露"；是连续的数值变量.

Both the "outcome" and "exposure" are continuous numerical variables.

我的问题是，如果说我运行模型和beta估计值，则CI为95％，p值看起来像下面的样子:

My question is, if say I run the model and the beta estimate, 95% CI, and p-value looks something like the one below:

B = -0.20 // 95%CI: [-0.50, -0.001] // p = 0.04 

将其解释为:每增加1单位暴露量，结果就会减少0.20"?

Would it be appropriate to interpret this as: "For every 1 unit increase of the exposure is a 0.20 decrease in the outcome"?

我想知道的是它如何确定每1单位增加"的顺序?这仅仅是R在回归模型中运行时如何对连续变量进行排序的默认顺序吗?另外，由于我的结果和暴露都是连续变量，这是否意味着我在运行模型时会自动将这些变量按升序排序(默认情况下?)?

What I want to know is how did it determine the order of "per 1 unit increase"? Is that just the default order of how R sorts continuous variables when running it in a regression model? Also, since both my outcome and exposure are continuous variables, does this mean that it automatically sorted these variables in ascending order (by default?) when I ran the model?

在我使用连续变量运行任何回归模型之前，对于此排序顺序是否重要有些困惑.任何提示/帮助将不胜感激！

Just a bit confused on whether this sorting order matters before I run any regression model using continuous variables. Any tips / help would be appreciated!

推荐答案

在OLS中，没有对预测变量进行排序或排序.在从左侧减去等式之前，先对等式的右侧求和.然后，将这种差异的平方最小化.因此，使用这种技术，预测变量就不必进行任何排序.

Under OLS, there is no ordering or sorting of the predictors. The right hand side of the equation is summed before subtracting it from the left hand side. Then the square of this difference is minimized. So with this technique, the predictors do not have to be sorted in any way.

为了解释您的Beta，预测变量应该是独立的，因此您按顺序购买它们无关紧要.旁注:实际上，您可能会在预测变量之间产生某种依赖性，这将反映在标准误差稍大的情况下.

For interpretation of your betas, the predictors are supposed to be independent, so it doesn't matter in which order you take them. Side note: In reality, you might get some dependence among the predictors, and this will be reflected in the standard errors being slightly larger.

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