如何在 r 中合并稳健混合 model 的 MI 置信区间？

Question

我可以运行rlmer model 和由mice产生的 object ，但是当我尝试合并结果时，会收到消息Error: No tidy method for objects of class rlmerMod 。 有替代方案吗？

下面是我的数据和模型的可重现示例：

set.seed(1)
library(data.table)
library(robustlmm)
library(mice)
library(miceadds)

dt <- data.table(id = rep(1:10, each=3), 
                 group = rep(1:2, each=15),
                 time = rep(1:3, 10),
                 sex = rep(sample(c("F","M"),10,replace=T), each=3),
                 x = rnorm(30),
                 y = rnorm(30))

setDT(dt)[id %in% sample(1:10,4) & time == 2, `:=` (x = NA, y = NA)][
          id %in% sample(1:10,4) & time == 3, `:=` (x = NA, y = NA)]
                        
# Multiple imputation -------------------------------------------------------------------

pm <- make.predictorMatrix(dt)
pm[,c('x','y')] <- 0
pm[c('x','y'), 'id'] <- -2
imp <- mice(dt, pred = pm, meth = "2l.pmm", seed = 1, m = 2, print = FALSE, maxit = 20)

# Modelling -----------------------------------------------------------------------------

m <- with(imp, rlmer(y ~ 1 + time * group + sex + (1 | id), REML=F))
pool.fit <- pool(m)

> pool.fit <- pool(m)
Error: No tidy method for objects of class rlmerMod
In addition: Warning message:
In get.dfcom(object, dfcom) : Infinite sample size assumed. # I don't get this warning using my real data

谢谢！

编辑：

正如@BenBolker 所评论的那样， library(broom.mixed)让pool.fit可以正常运行。 然而， summary(pool.fit,conf.int = TRUE)返回估计值，但NaN表示自由度、p 值和置信区间。

library(broom.mixed)

pool.fit <- pool(m)
summary(pool.fit,conf.int = TRUE)

         term    estimate std.error   statistic  df p.value 2.5 % 97.5 %
1 (Intercept) -1.31638288 1.2221584 -1.07709683 NaN     NaN   NaN    NaN
2        time  0.02819273 0.4734632  0.05954578 NaN     NaN   NaN    NaN
3       group  1.49581955 0.8776475  1.70435124 NaN     NaN   NaN    NaN
4        sexM -0.61383469 0.7137998 -0.85995356 NaN     NaN   NaN    NaN
5  time:group -0.25690287 0.3005254 -0.85484573 NaN     NaN   NaN    NaN

我不知道是否需要另一个参数（例如，用于定义 df 方法）。

现在，我尝试tbl_regression(m)但它也不起作用：

> tbl_regression(m)
pool_and_tidy_mice(): Tidying mice model with
`mice::pool(x) %>% mice::tidy(exponentiate = FALSE, conf.int = TRUE, conf.level = 0.95)`
Error in match.call() : ... used in a situation where it does not exist # how to correct this?
In addition: Warning message:
In get.dfcom(object, dfcom) : Infinite sample size assumed. # again, this warning don't occur with my original data

任何提示？

Answer 1

只需加载broom.mixed package，它有rlmerMod对象的 tidiers。 （ broom.mixed get_methods() function：

remotes::install_github("bbolker/broom.mixed")
library(broom.mixed)
print(get_methods(), n = Inf)

# A tibble: 22 × 4
   class     tidy  glance augment
   <chr>     <lgl> <lgl>  <lgl>  
 1 allFit    TRUE  TRUE   FALSE  
 2 brmsfit   TRUE  TRUE   TRUE   
 3 gamlss    TRUE  TRUE   FALSE  
 4 gamm4     TRUE  TRUE   TRUE   
 5 glmmadmb  TRUE  TRUE   TRUE   
 6 glmmTMB   TRUE  TRUE   TRUE   
 7 gls       TRUE  TRUE   TRUE   
 8 lme       TRUE  TRUE   TRUE   
 9 lmList4   TRUE  FALSE  FALSE  
10 mcmc      TRUE  FALSE  FALSE  
11 mcmc.list TRUE  FALSE  FALSE  
12 MCMCglmm  TRUE  FALSE  FALSE  
13 merMod    TRUE  TRUE   TRUE   
14 MixMod    TRUE  FALSE  FALSE  
15 ranef.mer FALSE FALSE  TRUE   
16 rjags     TRUE  FALSE  FALSE  
17 rlmerMod  TRUE  FALSE  FALSE  
18 stanfit   TRUE  FALSE  FALSE  
19 stanreg   TRUE  TRUE   FALSE  
20 TMB       TRUE  FALSE  FALSE  
21 varComb   TRUE  FALSE  FALSE  
22 varFunc   TRUE  FALSE  FALSE