R中的polr(..)序数逻辑回归 [英] polr(..) ordinal logistic regression in R

问题描述

使用polr功能时遇到麻烦.

I'm experiencing some trouble when using the polr function.

这是我拥有的数据的子集:

Here is a subset of the data I have:

# response variable
rep = factor(c(0.00, 0.04, 0.06, 0.13, 0.15, 0.05, 0.07, 0.00, 0.06, 0.04, 0.05, 0.00, 0.92, 0.95, 0.95, 1, 0.97, 0.06, 0.06, 0.03, 0.03, 0.08, 0.07, 0.04, 0.08, 0.03, 0.07, 0.05, 0.05, 0.06, 0.04, 0.04, 0.08, 0.04, 0.04, 0.04, 0.97, 0.03, 0.04, 0.02, 0.04, 0.01, 0.06, 0.06, 0.07, 0.08, 0.05, 0.03, 0.06,0.03))
# "rep" is discrete variable which represents proportion so that it varies between 0 and 1
# It is discrete proportions because it is the proportion of TRUE over a finite list of TRUE/FALSE. example: if the list has 3 arguments, the proportions value can only be 0,1/3,2/3 or 1

# predicted variable
set.seed(10)
pred.1 = sample(x=rep(1:5,10),size=50)
pred.2 = sample(x=rep(c('a','b','c','d','e'),10),size=50)
# "pred" are discrete variables 

# polr
polr(rep~pred.1+pred.2)

我给您的子集效果很好！但是我的整个数据集和其中的某些子集不起作用！除了数量，我在数据中找不到与该子集不同的任何内容.因此，这是我的问题:例如，在级别数方面是否存在任何限制，这些限制会导致以下错误消息:

The subset I gave you works fine ! But my entire data set and some subset of it does not work ! And I can't find anything in my data that differ from this subset except the quantity. So, here is my question: Is there any limitations in terms of the number of levels for example that would yield to the following error message:

Error in optim(s0, fmin, gmin, method = "BFGS", ...) : 
  the initial value in 'vmin' is not finite

和通知消息:

   glm.fit: fitted probabilities numerically 0 or 1 occurred

(我必须将这两个消息翻译成英文，所以它们可能不是100％正确)

(I had to translate these two messages into english so they might no be 100% correct)

有时我只会收到通知消息，有时一切都很好，这取决于我使用的数据子集是什么.

I sometimes only get the notification message and sometimes everything is fine depending on the what subset of my data I use.

我的rep变量总共有101个信息级别(除了我所描述的数据种类外，什么都没有)

My rep variable have a total of 101 levels for information (and contain nothing else than the kind of data I described)

所以我要问的是一个可怕的问题，因为我无法提供完整的数据集，而且我也不知道问题出在哪里.通过这些信息，您能猜出我的问题出在哪里吗?

So it is a terrible question that I am asking becaue I can't give you my full dataset and I don't know where is the problem. Can you guess where my problem comes from thanks to these informations ?

谢谢

推荐答案

orm来自 rms 可以处理大量类别的有序结果.

orm from rms can handle ordered outcomes with a large number of categories.

library(rms)
orm(rep ~ pred.1 + pred.2)

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