R中Logistic回归的虚拟变量

Update: Regarding the issue of regression coefficients relative to ordering of the factors. Changing the reference level will change the coefficients, because the coefficients will represent contrasts between different combinations of categories, but it won't change the predicted probabilities of a Yes or No outcome. (Regression modeling wouldn't be all that credible if you could change the predictions just by changing the reference category.) Note, for example, that the predicted probabilities are the same even if we switch the reference category for Priorconv:m1 = glm(cbind(Yes,No)~Priorconv+Crime+Priorconv:Crime,data=table1,family=binomial)predict(m1, type="response")1 2 3 4 5 6 7 80.9473684 0.8705882 0.9473684 0.8705882 0.7272727 0.6336634 0.7272727 0.6336634table2 = table1table2$Priorconv = relevel(table2$Priorconv, ref = "P")m2 = glm(cbind(Yes,No)~Priorconv+Crime+Priorconv:Crime,data=table2,family=binomial)predict(m2, type="response")1 2 3 4 5 6 7 80.9473684 0.8705882 0.9473684 0.8705882 0.7272727 0.6336634 0.7272727 0.6336634 这篇关于R中Logistic回归的虚拟变量的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！