# Average Marginal Effects interpretation when explanatory variables are ratios

In short, I am working with a classification problem where I have conducted a logistic regression. The dependent variable is a binary variable with the five explanatory variables being ratios ranging from 0 to 1. This is done using the caret package utilizing the train()-function. Furthermore, I have used the margins package and the margins()-function in order to obtain the Average Marginal Effects. For example, for one of the explanatory variables the AME is -0.05 and significant.

How do I interpret this? Is it so that a 1 unit increase (which equals a 100% increase?) in the explanatory variable will yield a 5% lower probability for an outcome of Y = 1?

• Hi Jonas, I would suggest not using the margins package to interpret a logistic regression model, and instead interpret the logistic regression coefficients on the odds scale.
– JTH
Dec 14 '20 at 16:44