glmulti/bestAIC筛选最佳子集时报错：
Warning message:
In fitter(X, Y, strats, offset, init, control, weights = weights, :
Loglik converged before variable 1,2 ; beta may be infinite.
Usually you can ignore the message, it is mostly for your information. The key things to note are a. When one of the coefficients goes to infinity in a Cox model, the Wald test of significance beta/se(beta) breaks down, and is no longer reliable. The LR test however is still valid. Hence routines like stepAIC are ok.
So are predicted values, residuals, etc etc. In fact it is pretty much only the Wald test that needs to be ignored: it is based on a Taylor series that simply doesn't work that far from zero. Oops -- confidence intervals based on the se are also useless.
b. The actual value of beta that is reported depends on the convergence criteria for the routine. So this is one case where different Cox model functions can give results that look different. I work in medical research, and view these differences as unimportant: if I were to tell you that your relative risk of death was exp(11) = 59,774 fold greater than your compatriots, would the message be substantially changed for beta of 10 or 12?
There is a statistical literature under the heading of "monotone likelihood ratio" that worries about these coefficients and tries to fix them. Much ado about nothing, IMHO. c. For a large beta and a very skewed covariate the message can sometimes be wrong. Beta is finite, just unstable. I might still prefer the LR to the Wald in this case. Spline fits based on the truncated power basis (which Frank Harrell uses) are one way to generate such spurious messages. Frank has argued with me that these messages may be shedding more confusion than illumination. He has a point.
Terry Therneau
- Cox模型中,一个系数趋向于无穷大时, Wald 检验/se(beta)故障且结果不可靠;而似然比检验仍然可靠,因此stepAIC仍然可用
- beta真实的值取决于该路径下的收敛原则,因此不同的cox模型功能得到不同的结果.但大多数情况下这种差异并不重要
- 结果是一个大的beta值(有,但不稳定)和一个斜率很大的协变量时,得到的信息可能不正确.此时更倾向于使用似然比检验,而不是Wald检验
