GLM 模型总结问题答案

【问题标题】：GLM Model Summary ProblemsGLM 模型总结问题
【发布时间】：2022-02-12 06:29:53
【问题描述】：

我在 R 中运行了以下 GLM 模型，但总结结果没有意义。 P值相同，系数非常大。出了点问题，但我不知道具体是什么。

mod.glm <- glm(factor(CloseWon) ~ days_to_close + factor(state) + number_of_times_contacted + number_of_sales_activities + factor(original_source) + average_pageviews + marketing_emails_delivered + sends_since_last_engagement, data = HSBI_train, family = binomial('logit'))

summary(mod)

下面是我的输出图像的链接，谢谢 [1]：https://i.stack.imgur.com/A5jsQ.jpg

按要求输入：

dput(HSBI)
structure(list(days_to_close = c(143L, 0L, 264L, 0L, 138L, 0L, 
117L, 48L, 258L, 0L, 59L, 125L, 204L, 260L, 0L, 210L, 0L, 119L, 
0L, 286L, 29L, 0L, 56L, 0L, 0L, 92L, 92L, 94L, 38L, 223L, 284L, 
0L, 289L, 278L, 128L, 52L, 137L, 0L, 256L, 0L, 119L, 175L, 225L, 
118L, 161L, 129L, 94L, 0L, 33L, 0L, 0L, 26L, 0L, 0L, 0L, 0L, 
163L, 112L, 0L, 0L, 0L, 6L, 0L, 0L, 8L, 0L, 0L, 251L, 0L, 0L, 
100L, 0L, 118L, 126L, 65L, 0L, 116L, 120L, 0L, 115L, 20L, 40L, 
0L, 77L, 0L, 0L, 0L, 0L, 0L, 0L, 184L, 0L, 268L, 0L, 49L, 128L, 
0L, 129L, 240L, 0L, 164L, 0L, 73L, 0L, 0L, 200L, 0L, 22L, 0L, 
0L, 0L, 0L, 0L, 268L, 20L, 0L, 0L, 31L, 99L, 0L, 0L, 0L, 0L, 
263L, 0L, 0L, 265L, 0L, 280L, 174L, 267L, 0L, 0L, 260L, 0L, 0L, 
0L, 219L, 0L, 0L, 292L, 0L, 259L, 0L, 0L, 114L, 127L, 127L, 0L, 
41L, 0L, 251L, 281L, 0L, 226L, 277L, 268L, 0L, 219L, 0L, 97L, 
0L, 0L, 0L, 218L, 0L, 98L, 64L, 0L, 0L, 101L, 0L, 0L, 0L, 0L, 
0L, 165L, 0L, 0L, 0L, 0L, 76L, 48L, 233L, 0L, 0L, 0L, 0L, 0L, 
0L, 107L, 189L, 0L, 94L, 19L, 223L, 128L, 0L, 0L, 0L, 106L, 246L, 
0L, 0L, 118L, 168L, 160L, 0L, 225L, 231L, 222L, 0L, 0L, 122L, 
0L, 37L, 236L, 246L, 0L, 16L, 0L, 70L, 0L, 123L, 264L, 0L, 0L, 
0L, 0L, 264L, 0L, 41L, 296L, 124L, 198L, 0L, 0L, 0L, 58L, 156L, 
166L, 274L, 0L, 88L, 2L, 0L, 124L, 0L, 80L, 41L, 278L, 0L, 0L, 
252L, 0L, 80L, 0L, 0L), state = c("", "fl", "fl", "sj", "ga", 
"nc", "ga", "in", "ga", "ca", "va", "ca", "va", "tn", "co", "tx", 
"fl", "tn", "ca", "tn", "in", "ga", "il", "nj", "ca", "ga", "ga", 
"", "", "ga", "ga", "fl", "ga", "nc", "ga", "tx", "", "ga", "ga", 
"dc", "ny", "tn", "fl", "va", "ga", "al", "ca", "nv", "ca", "ga", 
"sc", "va", "ga", "oh", "ga", "fl", "la", "tn", "ny", "fl", "ca", 
"ca", "wa", "ny", "il", "ga", "ca", "fl", "fl", "al", "al", "fl", 
"al", "tn", "sc", "", "fl", "ga", "az", "fl", "ga", "", "ca", 
"ga", "ga", "oh", "ga", "al", "ga", "", "tx", "sc", "ga", "ny", 
"nc", "tn", "co", "oh", "al", "tx", "", "co", "ne", "ny", "fl", 
"oh", "ga", "ia", "va", "fl", "sc", "ca", "tn", "ga", "co", "ok", 
"ga", "al", "tx", "", "fl", "md", "ga", "al", "tn", "wa", "al", 
"oh", "ga", "ga", "", "pa", "oh", "al", "ny", "az", "tn", "oh", 
"ga", "tx", "ga", "tx", "tn", "va", "fl", "ga", "ga", "fl", "ny", 
"fl", "az", "ga", "ga", "tn", "ga", "", "ga", "pa", "fl", "tn", 
"al", "ga", "al", "ga", "ms", "vt", "ca", "fl", "fl", "ky", "oh", 
"wa", "fl", "wa", "ga", "az", "il", "al", "nc", "al", "nj", "tx", 
"tx", "fl", "la", "ga", "nc", "", "ga", "al", "tx", "oh", "fl", 
"tn", "tn", "fl", "ga", "ca", "", "ca", "fl", "ga", "fl", "dc", 
"md", "fl", "tx", "oh", "tx", "", "al", "tx", "fl", "tn", "tx", 
"ny", "ny", "tn", "az", "ga", "al", "tx", "sc", "tn", "tn", "ca", 
"al", "tn", "fl", "al", "tn", "ga", "ga", "ga", "fl", "pa", "tx", 
"co", "sc", "tx", "tx", "ga", "ny", "ma", "ny", "fl", "fl", "ca", 
"tn", "nv", "ga", "ga", "tx", "", "or", "", "fl", "il"), number_of_times_contacted = c(7L, 
5L, 7L, 2L, 40L, 4L, 6L, 6L, 5L, 1L, 3L, 5L, 8L, 8L, 1L, 10L, 
4L, 9L, 3L, 10L, 6L, 4L, 7L, 7L, 2L, 9L, 1L, 5L, 3L, 9L, 11L, 
4L, 8L, 10L, 10L, 6L, 10L, 3L, 10L, 12L, 5L, 7L, 8L, 5L, 31L, 
10L, 6L, 1L, 5L, 20L, 15L, 7L, 3L, 3L, 6L, 6L, 6L, 7L, 2L, 3L, 
1L, 2L, 1L, 19L, 2L, 3L, 1L, 10L, 5L, 3L, 7L, 2L, 8L, 9L, 3L, 
3L, 8L, 12L, 1L, 7L, 2L, 2L, 4L, 50L, 6L, 4L, 2L, 3L, 1L, 9L, 
7L, 3L, 14L, 1L, 3L, 5L, 7L, 4L, 8L, 4L, 6L, 2L, 4L, 7L, 5L, 
7L, 7L, 6L, 5L, 6L, 4L, 1L, 5L, 8L, 2L, 1L, 6L, 3L, 4L, 4L, 4L, 
1L, 6L, 8L, 4L, 3L, 8L, 11L, 10L, 6L, 8L, 5L, 8L, 6L, 4L, 2L, 
10L, 8L, 6L, 8L, 8L, 8L, 8L, 5L, 6L, 4L, 5L, 9L, 12L, 4L, 1L, 
11L, 12L, 7L, 9L, 14L, 8L, 3L, 7L, 17L, 8L, 4L, 2L, 33L, 9L, 
1L, 4L, 8L, 6L, NA, 7L, 3L, 9L, 2L, 5L, 8L, 6L, 6L, 4L, 10L, 
3L, 4L, 3L, 12L, 24L, 9L, 3L, 11L, 3L, 19L, 3L, 7L, 4L, 9L, 6L, 
7L, 10L, 1L, 1L, 1L, 4L, 10L, 1L, 3L, 5L, 7L, 6L, 2L, 7L, 9L, 
8L, 10L, 6L, 8L, 2L, 3L, 8L, 9L, 1L, 3L, 13L, 10L, 5L, 9L, 8L, 
1L, 6L, 12L, 4L, 11L, 10L, 4L, 10L, 10L, 7L, 6L, 3L, 4L, 3L, 
6L, 6L, 10L, 4L, 9L, 2L, 21L, 9L, 1L, 4L, 3L, 21L, 8L, 5L, 10L, 
3L, 8L, 8L, 7L), number_of_sales_activities = c(8L, 5L, 7L, 2L, 
61L, 4L, 7L, 6L, 5L, 1L, 3L, 5L, 8L, 8L, 1L, 10L, 4L, 9L, 3L, 
10L, 6L, 4L, 7L, 9L, 2L, 9L, 2L, 5L, 3L, 9L, 11L, 4L, 8L, 10L, 
10L, 6L, 14L, 3L, 10L, 14L, 5L, 8L, 8L, 5L, 43L, 10L, 7L, 1L, 
5L, 22L, 21L, 7L, 3L, 3L, 7L, 6L, 6L, 7L, 2L, 3L, 1L, 2L, 1L, 
28L, 2L, 3L, 1L, 10L, 5L, 4L, 7L, 2L, 8L, 9L, 3L, 3L, 8L, 12L, 
1L, 7L, 2L, 2L, 4L, 98L, 6L, 6L, 2L, 3L, 1L, 9L, 7L, 4L, 17L, 
2L, 3L, 5L, 7L, 4L, 8L, 4L, 7L, 2L, 5L, 8L, 5L, 7L, 8L, 6L, 5L, 
6L, 4L, 1L, 5L, 8L, 2L, 1L, 6L, 3L, 4L, 4L, 4L, 1L, 6L, 9L, 4L, 
3L, 8L, 16L, 10L, 6L, 8L, 5L, 9L, 6L, 4L, 2L, 10L, 8L, 7L, 8L, 
8L, 8L, 8L, 5L, 6L, 4L, 5L, 9L, 15L, 4L, 1L, 11L, 12L, 9L, 10L, 
21L, 8L, 4L, 7L, 21L, 8L, 4L, 2L, 61L, 9L, 1L, 4L, 8L, 7L, NA, 
7L, 3L, 9L, 2L, 5L, 9L, 6L, 6L, 4L, 12L, 3L, 5L, 3L, 12L, 35L, 
17L, 3L, 12L, 3L, 28L, 3L, 8L, 4L, 9L, 6L, 7L, 10L, 1L, 1L, 1L, 
4L, 10L, 1L, 3L, 5L, 7L, 6L, 2L, 7L, 10L, 8L, 10L, 6L, 8L, 3L, 
3L, 8L, 9L, 1L, 3L, 20L, 10L, 7L, 9L, 8L, 1L, 6L, 12L, 4L, 12L, 
10L, 5L, 10L, 11L, 7L, 7L, 3L, 4L, 3L, 6L, 6L, 11L, 4L, 9L, 2L, 
26L, 9L, 1L, 4L, 3L, 39L, 8L, 6L, 10L, 3L, 8L, 8L, 7L), original_source = c("Direct Traffic", 
"Paid Social", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Organic Search", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Paid Social", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Paid Social", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Paid Social", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Paid Social", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Paid Social", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Paid Social", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Paid Social", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Organic Search", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Organic Social", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Paid Social", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Paid Social", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Paid Social", "Direct Traffic", 
"Paid Social", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Paid Social", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Paid Social", "Paid Social", "Direct Traffic", "Direct Traffic", 
"Paid Social", "Paid Social", "Direct Traffic", "Direct Traffic", 
"Organic Search", "Direct Traffic", "Direct Traffic", "Direct Traffic", 
"Direct Traffic"), average_pageviews = c(1L, 1L, 2L, 2L, 1L, 
2L, 1L, 2L, 7L, 2L, 2L, 1L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 
4L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 5L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 
3L, 3L, 2L, 1L, 1L, 4L, 3L, 2L, 2L, 4L, 1L, 2L, 3L, 2L, 1L, 2L, 
5L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 5L, 6L, 1L, 1L, 4L, 
2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 5L, 2L, 2L, 2L, 6L, 
3L, 2L, 2L, 2L, 2L, 1L, 2L, 5L, 1L, 2L, 3L, 2L, 2L, 3L, 1L, 3L, 
2L, 2L, 3L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 4L, 2L, 3L, 1L, 1L, 
1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 
3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 4L, 
4L, 1L, 2L, 2L, 0L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 
4L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L), marketing_emails_delivered = c(15L, 
6L, 13L, 7L, 65L, 10L, 11L, 11L, 22L, 3L, 8L, 12L, 17L, 15L, 
2L, 18L, 9L, 11L, 9L, 20L, 10L, 12L, 11L, 14L, 8L, 11L, 11L, 
6L, 8L, 19L, 21L, 3L, 20L, 22L, 15L, 11L, 14L, 8L, 21L, 21L, 
11L, 14L, 15L, 11L, 47L, 16L, 10L, 9L, 8L, 22L, 14L, 10L, 8L, 
2L, 13L, 15L, 16L, 16L, 2L, 7L, 2L, 9L, 9L, 21L, 9L, 8L, 3L, 
21L, 15L, 2L, 11L, 2L, 11L, 16L, 8L, 3L, 16L, 17L, 2L, 16L, 8L, 
8L, 9L, 44L, 19L, 9L, 9L, 9L, 3L, 12L, 7L, 9L, 22L, 7L, 8L, 12L, 
12L, 12L, 21L, 5L, 16L, 2L, 3L, 18L, 15L, 15L, 21L, 10L, 9L, 
10L, 12L, 3L, 13L, 22L, 9L, 6L, 17L, 7L, 10L, 3L, 3L, 9L, 13L, 
22L, 14L, 9L, 22L, 8L, 23L, 13L, 20L, 9L, 10L, 22L, 9L, 9L, 18L, 
19L, 13L, 19L, 20L, 18L, 13L, 7L, 10L, 11L, 12L, 16L, 19L, 8L, 
3L, 13L, 24L, 15L, 28L, 24L, 22L, 4L, 19L, 31L, 15L, 9L, 2L, 
44L, 11L, 2L, 10L, 13L, 18L, 3L, 15L, 9L, 19L, 8L, 6L, 9L, 16L, 
10L, 5L, 19L, 2L, 9L, 8L, 20L, 14L, 8L, 9L, 21L, 7L, 21L, 11L, 
14L, 19L, 15L, 10L, 19L, 16L, 1L, 9L, 3L, 10L, 21L, 3L, 3L, 11L, 
15L, 16L, 2L, 11L, 21L, 11L, 22L, 16L, 16L, 9L, 8L, 21L, 21L, 
3L, 1L, 20L, 13L, 9L, 16L, 13L, 6L, 10L, 15L, 3L, 20L, 11L, 8L, 
21L, 16L, 14L, 8L, 9L, 15L, 8L, 16L, 9L, 23L, 9L, 15L, 2L, 22L, 
16L, 3L, 9L, 8L, 31L, 19L, 18L, 21L, 9L, 14L, 19L, 10L), CloseWon = c(1, 
0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 
1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 
1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 
0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 
0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 
0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 
0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 
1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 
0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 
1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 
1, 0, 1, 0, 0)), class = "data.frame", row.names = c(NA, -258L
))

【问题讨论】：

你可以只发布summary(mod)的明文输出而不是屏幕照片吗？我看不懂。
我可以复制和粘贴，但我只是重新上传了一张这次应该可以阅读的照片
您能附上您的数据吗？我怀疑你的因变量变化很小，可以解释你得到什么，但这只是一个猜测
鉴于提供的信息，无法回答此问题。值得一提的是，我看到所有 RHS 变量的 p 值都非常小，样本量很大。
我一直在逻辑回归的左侧使用数字 0/1 变量。当您在公式中使用 CloseWon（不强制转换）时会发生什么？

标签： r glm

【解决方案1】：

您的回归器days_to_close 有问题。请注意，在这个简单的回归中，拟合概率为 0 或 1 时会出现错误。

> mod.glm <- glm(factor(CloseWon) ~ days_to_close, data=data,
+                family = binomial('logit'))
Warning messages:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred

要了解发生这种情况的原因，请注意

> table(data$days_to_close > 0, data$CloseWon)
       
          0   1
  FALSE 124   2
  TRUE    0 132

因此，您会看到，无论您的其他回归器获得什么值，只要 days_to_close 大于零，CloseWon 始终为 0。粗略地说，这意味着您尝试优化的函数获得了 +oo 的值。优化算法会因此而跳闸，最终将您的其他回归系数推到巨大的值。

关于如何处理完美分离的论文有很多。您可以开始，例如，here。

【讨论】：

谢谢。很有帮助