r - 如何让 polr 优雅地失败

Question

我的 RMarkdown 脚本和序数逻辑回归拟合的迭代应用存在问题，该问题已得到充分讨论（原因是）here 和 here。我还尝试按照建议的here 将maxint 增加到100 无效。

我知道整体代码很好，因为到目前为止它似乎只在数百个模型中失败了一个。

脚本中断错误是：

Error in polr(a, data = rData, Hess = TRUE) : 
  attempt to find suitable starting values failed
In addition: Warning messages:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 

我的问题是；有没有办法让模型优雅地拟合失败并报告cat(Model XY doesn't converge, moving on) 之类的内容，然后继续输入列表中的下一个模型？

我怀疑这将涉及将调用包装在条件测试中，可能看起来像这样：

if( polr(...) == FAILS) {
  cat(message)
  return() # exit out of current iteration
} else {
  polr(... # run the model fit normally
}

这是失败的数据和模型：

## Raw Data
T_P <- c(32,34,31,24,40,21,30,31,25,31,18,32,26,26,27,35,22,32,27,28)
T_M <- c(16,6,12,12,13,10,14,14,11,13,5,13,9,13,11,18,11,15,12,13)
E   <- c(10,15,11,15,15,8,14,13,15,12,9,11,13,15,9,15,6,13,6,15)
Q13.1.2 <- c(5,4,5,5,4,4,5,4,3,5,3,4,3,5,4,4,4,5,5,4) # categorical response

## Dataframe of data
rData <- data.frame(T_P,T_M,E,Q13.1.2)

d <- "Q13.1.2"       # dependant variable
c <- "T_P + T_M + E" # sting of covariates
a <- str_c("as.factor(",d,") ~ ", c, sep="") # concat depVar & indVars into model alogrithm

m <- polr(a, data=rData, Hess=TRUE) # build model

Answer 1

原来在 R 中有一些错误处理，尽管是初级的（参见 here）。遵循这个逻辑，记住示例实际上是循环的一部分：

tryCatch({
           # this is the test (just run the call in question)
           polr(a, data=rData, Hess=TRUE)
         }, warning = function(w) {
            # this is how tryCatch handles a stop event
            message("Model doesn't fit") # message to console
            modelFail <<- TRUE # set a variable to test                 
         }, error = function(e) {
            # this is tryCatch handing an error event
            message("error!")
         }, finally = {
            # this is what to do in the event of an.. event
            # I'm not using this approach
         })

if(exists("modelFail")){
  cat("DON'T PANIC! The model fit failed, moving on...")
  rm(modelFail) # I clear the flag to reuse in case of another failure in loop
  return()
} else {
    # now it is safe to run the fit as we have handled an error above                  
    m <- polr(a, data=rData, Hess=TRUE) # build model
}