我有一个包含分类变量和连续变量的数据集“ ex”。响应变量为A16。我想建立一个逻辑回归模型，但收到一条消息：
glm.fit：发生数字0或1的拟合概率
，我怀疑数据集结构有些问题。尽管我知道对于R中的GLM模型来说因素应该不是问题。 这是数据集：
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16
68 b 19.42 6.500 u g w h 1.460 t t 7 f g 80.0000 2954 1
69 b 35.17 25.125 u g x h 1.625 t t 1 t g 515.0000 500 1
70 b 32.33 7.500 u g e bb 1.585 t f 0 t s 420.0000 0 0
71 b 34.83 4.000 u g d bb 12.500 t f 0 t g 280.2858 0 0
72 a 38.58 5.000 u g cc v 13.500 t f 0 t g 980.0000 0 0
73 b 44.25 0.500 u g m v 10.750 t f 0 f s 400.0000 0 0
74 b 44.83 7.000 y p c v 1.625 f f 0 f g 160.0000 2 0
75 b 20.67 5.290 u g q v 0.375 t t 1 f g 160.0000 0 0
76 b 34.08 6.500 u g aa v 0.125 t f 0 t g 443.0000 0 0
77 a 19.17 0.585 y p aa v 0.585 t f 0 t g 160.0000 0 0
78 b 21.67 1.165 y p k v 2.500 t t 1 f g 180.0000 20 0
data.frame': 11 obs. of 16 variables:
$ A1 : Factor w/ 2 levels "a","b": 2 2 2 2 1 2 2 2 2 1 ...
$ A2 : num 19.4 35.2 32.3 34.8 38.6 ...
$ A3 : num 6.5 25.1 7.5 4 5 ...
$ A4 : Factor w/ 3 levels "l","u","y": 2 2 2 2 2 2 3 2 2 3 ...
$ A5 : Factor w/ 3 levels "g","gg","p": 1 1 1 1 1 1 3 1 1 3 ...
$ A6 : Factor w/ 14 levels "aa","c","cc",..: 13 14 5 4 3 10 2 11 1 1 ...
$ A7 : Factor w/ 9 levels "bb","dd","ff",..: 4 4 1 1 8 8 8 8 8 8 ...
$ A8 : num 1.46 1.62 1.58 12.5 13.5 ...
$ A9 : Factor w/ 2 levels "f","t": 2 2 2 2 2 2 1 2 2 2 ...
$ A10: Factor w/ 2 levels "f","t": 2 2 1 1 1 1 1 2 1 1 ...
$ A11: int 7 1 0 0 0 0 0 1 0 0 ...
$ A12: Factor w/ 2 levels "f","t": 1 2 2 2 2 1 1 1 2 2 ...
$ A13: Factor w/ 3 levels "g","p","s": 1 1 3 1 1 3 1 1 1 1 ...
$ A14: num 80 515 420 280 980 ...
$ A15: int 2954 500 0 0 0 0 2 0 0 0 ...
$ A16: num 1 1 0 0 0 0 0 0 0 0 ...
dput(ex)
structure(list(A1 = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L), .Label = c("a", "b"), class = "factor"), A2 = c(19.42,
35.17, 32.33, 34.83, 38.58, 44.25, 44.83, 20.67, 34.08, 19.17,
21.67), A3 = c(6.5, 25.125, 7.5, 4, 5, 0.5, 7, 5.29, 6.5, 0.585,
1.165), A4 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
3L, 3L), .Label = c("l", "u", "y"), class = "factor"), A5 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L), .Label = c("g", "gg",
"p"), class = "factor"), A6 = structure(c(13L, 14L, 5L, 4L, 3L,
10L, 2L, 11L, 1L, 1L, 9L), .Label = c("aa", "c", "cc", "d", "e",
"ff", "i", "j", "k", "m", "q", "r", "w", "x"), class = "factor"),
A7 = structure(c(4L, 4L, 1L, 1L, 8L, 8L, 8L, 8L, 8L, 8L,
8L), .Label = c("bb", "dd", "ff", "h", "j", "n", "o", "v",
"z"), class = "factor"), A8 = c(1.46, 1.625, 1.585, 12.5,
13.5, 10.75, 1.625, 0.375, 0.125, 0.585, 2.5), A9 = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L), .Label = c("f",
"t"), class = "factor"), A10 = structure(c(2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L), .Label = c("f", "t"), class = "factor"),
A11 = c(7L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L), A12 = structure(c(1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L), .Label = c("f",
"t"), class = "factor"), A13 = structure(c(1L, 1L, 3L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L), .Label = c("g", "p", "s"), class = "factor"),
A14 = c(80, 515, 420, 280.285801010101, 980, 400, 160, 160,
443, 160, 180), A15 = c(2954L, 500L, 0L, 0L, 0L, 0L, 2L,
0L, 0L, 0L, 20L), A16 = c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
)), row.names = 68:78, class = "data.frame")
这是模型的代码：
fitex1 <- glm(A16~., data=ex_train, family=binomial)
fitex0 <- glm(A16~1, data=ex_train, family=binomial)