R，将 tbl_regression 与 glmer 一起使用时，获取错误消息“x”必须是至少二维的数组答案

【问题标题】：R, Getting the error message 'x' must be an array of at least two dimensions when using tbl_regression with glmerR，将 tbl_regression 与 glmer 一起使用时，获取错误消息“x”必须是至少二维的数组
【发布时间】：2021-06-23 18:41:50
【问题描述】：

我在运行以下代码时收到错误消息Error in colSums(tcm * y * w) : 'x' must be an array of at least two dimensions：

glmer(outcome ~ predictor + (1|id), 
      data = df, 
      family = binomial) %>% 
  tbl_regression()

就上下文而言，我希望查看结果（患者对就诊的满意度，1 = 满意，0 = 不满意）是否会根据医生是否完成研讨会而发生变化。之所以使用 GLMER 而不是 GLM，是因为每个医生有多个患者。因此，它是重复测量。

重现错误信息：

outcome <- c(1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,0,NA,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,0,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,NA ,1,1,1,1,1,1,1,1,1,NA,1,NA,NA,1,1,1,NA,0,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1)

predictor  <- c("91 Days Before", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days Before",
"91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days After", "91 Days Before", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days Before",
"91 Days After", "91 Days After", "91 Days Before", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days Before", "91 Days Before", "91 Days After",  "91 Days Before",  "91 Days Before",
"91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days After  ", "91 Days After",
"91 Days Before", "91 Days Before", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days After",
"91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days Before",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days Before",  "91 Days After",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
"91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days After", "91 Days After", "91 Days Before",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days Before", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days Before",
"91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days After", "91 Days Before", "91 Days After",  "91 Days After  ", "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days Before", "91 Days After", "91 Days After",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days Before", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days Before",
"91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
"91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
"91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days After  ", "91 Days Before",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After  ", "91 Days After",
"91 Days Before")

id <- c(46, 109, 82, 98, 98, 104,  98,  82,  27,  27,  25, 104,  44,  77, 102,44, 25, 104,  82,  66,  25,  66,  66,  66,  66, 111,  25, 111, 111,  46, 111, 46, 25, 25, 32,  25, 25, 25, 46,  25,  46,  25, 111,  32, 104, 111,  32, 111, 109,51, 32,  36,   4, 104,  32,  44,  34,  34,  19, 102,  65,  65,  65,  66,  66, 66, 66, 66, 66,  66, 66, 66, 66,  66,  66,  66,  66,  43,  43,  43,  43,  43,  43,43, 68,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49, 49, 49, 49, 49,  49, 49, 49, 49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,49, 49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49, 49, 49, 49, 49,  49, 49, 53, 53,  53,  53,  53,  53,  53,  53,  53,  53,  53,  53,53, 53,  97,  97,  84,  84,  84,  84,  84,  84,  84,  84,  84,  84,  84,  84, 84, 84, 84, 84,  84, 84, 84, 84,  84,  84,  84,  84,  84,  84,  29,  29,  29,  29,29, 29,  29,  13,  13,  13,  13,  13,  13,  13,  13,  13,  34,  34,  34,  24, 24, 24, 24, 24,  32, 32, 32, 32,  32,  76,  17,  17,  14,  14,  93,  85,  85,   6, 6,  6,   6,   6,   6,   6,   6,   6,   6,   6,  28,  28,  28,  28,  28,  28, 28, 28, 28, 28,  88, 88, 88, 88,  88,  88,  51,  51,  51,  51,  51,  51,  51,  92,92, 92,  45,  45,  45,  90,  90,  90,  90,  90,  58,  58,  58,  58,  58,  58, 58, 58, 83, 4,   4, 39, 81, 94,  64,  64,  64,  64,  64,  64,  64,  64,  64,  64,64, 64) 

ex_data <- cbind(id, predictor, outcome) %>% 
  as.data.frame() %>% 
  mutate(id = factor(id), 
         predictor = factor(predictor), 
         outcome = factor(outcome))

glmer(outcome ~ predictor + (1|id), 
      data = ex_data, 
      family = binomial) %>% 
  tbl_regression()

【问题讨论】：

标签： r lme4 gtsummary

【解决方案1】：

tl;dr gtsummary() 不知道如何处理作为因素的响应（base-R 以及扩展 glmer，对于它接受的方式几乎是病态的灵活二项式回归的响应值，但通常最好使用数值）。

在gtsummary issues list 发布问题将是一项公共服务。

避免使用cbind()，因为这会自动将所有向量强制为同一类型。

library(tidyverse)
library(gtsummary)

ex_data <- tibble(id, predictor, outcome) %>% 
  mutate(across(c(id, predictor), factor)
g1 <- glmer(outcome ~ predictor + (1|id), 
      data = ex_data, 
      family = binomial)
tbl_regression(g1)

保存glmer 拟合而不是将其直接传递给tbl_regression() 可能更有意义，因为您可能想用它做其他事情（诊断图、系数/点须图、预测、模型比较...... .)

【讨论】：

非常感谢！这完全有道理，我可以将其添加到 gtsummary 问题列表中！

【解决方案2】：

tbl_regression() 依赖于broom.helpers 包来准备和改进tidy() 返回的小标题。您可以使用broom.helpers::tidy_plus_plus() 查看broom.helpers 所做的不同增强。

您提到的错误已在broom.helpers 的当前开发版本中识别并修复。参照。 https://github.com/larmarange/broom.helpers/issues/116

outcome <- c(1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,0,NA,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,0,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,NA,1,1,1,NA ,1,1,1,1,1,1,1,1,1,NA,1,NA,NA,1,1,1,NA,0,1,1,1,1,NA,1,1,1,1,1,1,1,1,1,1,1,1)

predictor  <- c("91 Days Before", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days Before",
                "91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days After", "91 Days Before", "91 Days After", "91 Days After",  "91 Days After", "91 Days Before",
                "91 Days After", "91 Days After", "91 Days Before", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days Before", "91 Days Before", "91 Days After",  "91 Days Before",  "91 Days Before",
                "91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days After", "91 Days After",
                "91 Days Before", "91 Days Before", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days After",
                "91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days Before",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days Before",  "91 Days After",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
                "91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days After", "91 Days After", "91 Days Before",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days Before", "91 Days After", "91 Days After",  "91 Days After", "91 Days Before",
                "91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days After", "91 Days Before", "91 Days After",  "91 Days After", "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days Before", "91 Days After", "91 Days After",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days Before", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days Before", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days Before",
                "91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days After",
                "91 Days After", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days Before",  "91 Days Before",
                "91 Days Before", "91 Days After", "91 Days Before", "91 Days Before",  "91 Days After", "91 Days Before",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days After", "91 Days After", "91 Days After", "91 Days After",  "91 Days After", "91 Days After",
                "91 Days Before")

id <- c(46, 109, 82, 98, 98, 104,  98,  82,  27,  27,  25, 104,  44,  77, 102,44, 25, 104,  82,  66,  25,  66,  66,  66,  66, 111,  25, 111, 111,  46, 111, 46, 25, 25, 32,  25, 25, 25, 46,  25,  46,  25, 111,  32, 104, 111,  32, 111, 109,51, 32,  36,   4, 104,  32,  44,  34,  34,  19, 102,  65,  65,  65,  66,  66, 66, 66, 66, 66,  66, 66, 66, 66,  66,  66,  66,  66,  43,  43,  43,  43,  43,  43,43, 68,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49, 49, 49, 49, 49,  49, 49, 49, 49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,49, 49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49,  49, 49, 49, 49, 49,  49, 49, 53, 53,  53,  53,  53,  53,  53,  53,  53,  53,  53,  53,53, 53,  97,  97,  84,  84,  84,  84,  84,  84,  84,  84,  84,  84,  84,  84, 84, 84, 84, 84,  84, 84, 84, 84,  84,  84,  84,  84,  84,  84,  29,  29,  29,  29,29, 29,  29,  13,  13,  13,  13,  13,  13,  13,  13,  13,  34,  34,  34,  24, 24, 24, 24, 24,  32, 32, 32, 32,  32,  76,  17,  17,  14,  14,  93,  85,  85,   6, 6,  6,   6,   6,   6,   6,   6,   6,   6,   6,  28,  28,  28,  28,  28,  28, 28, 28, 28, 28,  88, 88, 88, 88,  88,  88,  51,  51,  51,  51,  51,  51,  51,  92,92, 92,  45,  45,  45,  90,  90,  90,  90,  90,  58,  58,  58,  58,  58,  58, 58, 58, 83, 4,   4, 39, 81, 94,  64,  64,  64,  64,  64,  64,  64,  64,  64,  64,64, 64) 

library(tidyverse)
library(broom.mixed)

ex_data <- cbind(id, predictor, outcome) %>% 
  as.data.frame() %>% 
  dplyr::mutate(id = factor(id), 
         predictor = factor(predictor), 
         outcome = factor(outcome))


lme4::glmer(outcome ~ predictor + (1|id), 
      data = ex_data, 
      family = binomial) %>%
  broom.helpers::tidy_plus_plus(exponentiate = TRUE, intercept = TRUE) %>%
  knitr::kable()

term	original_term	variable	var_label	var_class	var_type	var_nlevels	contrasts	contrasts_type	reference_row	label	n_obs	n_event	effect	group	estimate	std.error	statistic	p.value	conf.low	conf.high
(Intercept)	(Intercept)	(Intercept)	(Intercept)	NA	intercept	NA	NA	NA	NA	(Intercept)	270	264	fixed	NA	2.056839e+04	5.618032e+04	3.6360619	0.0002768	97.3273899	4.346757e+06
predictor91 Days After	NA	predictor	predictor	factor	dichotomous	2	contr.treatment	treatment	TRUE	91 Days After	137	134	NA	NA	1.000000e+00	NA	NA	NA	NA	NA
predictor91 Days Before	predictor91 Days Before	predictor	predictor	factor	dichotomous	2	contr.treatment	treatment	FALSE	91 Days Before	133	130	fixed	NA	6.825077e-01	7.266704e-01	-0.3587669	0.7197695	0.0846892	5.500312e+00
id.sd__(Intercept)	id.sd__(Intercept)	id.sd__(Intercept)	id.sd__(Intercept)	NA	ran_pars	NA	NA	NA	NA	id.sd__(Intercept)	NA	NA	ran_pars	id	8.898623e+00	NA	NA	NA	NA	NA

^{由reprex package (v2.0.0) 于 2021 年 6 月 24 日创建}

【讨论】：