it is called perfect prediction.

http://www.ats.ucla.edu/stat/mul ... on_logit_models.htm


A complete separation happens when the outcome variable separates a predictor variable or a combination of predictor variables completely. Albert and Anderson (1984) define this as, "there is a vector α that correctly allocates all observations to their group." Below is a small example.

Y X1 X2
0 1  3
0 2  2
0 3 -1
0 3 -1
1 5  2
1 6  4
1 10 1
1 11 0

In this example, Y is the outcome variable, X1 and X2 are predictor variables. We can see that observations with Y = 0 all have values of X1<=3 and observations with Y = 1 all have values of X1>3. In other words, Y separates X1 perfectly. The other way to see it is that X1 predicts Y perfectly since X1<=3 corresponds to Y = 0 and X1 > 3 corresponds to Y = 1. By chance, we have found a perfect predictor X1 for the outcome variable Y. In terms of predicted probabilities, we have Prob(Y = 1 | X1<=3) = 0 and Prob(Y=1 X1>3) = 1, without the need for estimating a model.

多谢大白菜！
第一次知道这种情况，以及怎么去处理。

刚做了一下研究，也有人针对这个问题做了解决方案。有兴趣，参加
http://artsandsciences.sc.edu/poli/psrw/Zorn_PA_Final.pdf

thanks for sharing

多谢大白菜帮我认清了问题

多谢分享文献。。。我还是选择删除这个样本吧，毕竟只有1个