r combine results from multiple lme4 objects-CodePudding

I am running a mixed effects model on my dataset ,

   library(lme4)
   data(cake)

each dataset is a subset of a larger datsaet

    subset(cake, recipe=="A")
    subset(cake, recipe=="B")
    subset(cake, recipe=="C")

I am using dlply to run my mixed effects model on each subset

   MxM1 <-   plyr::dlply(cake,
                   "recipe",
                     function(x)
                       lmer(angle ~ 1  (1|replicate)  temperature,
                          data=x))

This gives me a list of summaries based on each subset of data.

I know how to display the summaries one at a time using gt_summary package

       lm_cake$A %>%
            tbl_regression() %>%
               modify_column_hide(columns = ci) %>%
                  modify_column_unhide(columns = std.error)

   lm_cake$B %>%
            tbl_regression() %>%
               modify_column_hide(columns = ci) %>%
                  modify_column_unhide(columns = std.error)

   lm_cake$B %>%
            tbl_regression() %>%
               modify_column_hide(columns = ci) %>%
                  modify_column_unhide(columns = std.error)

I am not sure how to combine the results from all 3 objects (lm_cake$A, lm_cake$B, lm_cake$C) to display them as one summary table.

                Model: A                    Model: B           Model: C    
    Temperature            Beta    SE       Beta    SE       Beta    SE
        Temperature. L   
        Temperature. Q   
        Temperature. C   
        Temperature^4   
        Temperature^5

Any suggestions or help is much apricated. Thanks.

CodePudding user response：

You need something like this?:

library(lme4)
data(cake)

library(dplyr)
library(broom)
library(broom.mixed)

cake %>% 
  mutate(recipe = as_factor(recipe)) %>% 
  group_by(recipe) %>% 
  group_split() %>% 
  map_dfr(.f = function(df){
    lmer(angle ~ 1   (1|replicate)   temperature,
         data=df) %>% 
  tidy() %>% 
  add_column(recipe = unique(df$recipe), .before = 1)
  })

 A tibble: 24 × 7
   recipe effect   group     term            estimate std.error statistic
   <fct>  <chr>    <chr>     <chr>              <dbl>     <dbl>     <dbl>
 1 A      fixed    NA        (Intercept)       33.1        1.42    23.3  
 2 A      fixed    NA        temperature.L      6.43       1.22     5.26 
 3 A      fixed    NA        temperature.Q     -0.713      1.22    -0.583
 4 A      fixed    NA        temperature.C     -2.33       1.22    -1.90 
 5 A      fixed    NA        temperature^4     -3.35       1.22    -2.74 
 6 A      fixed    NA        temperature^5     -0.151      1.22    -0.124
 7 A      ran_pars replicate sd__(Intercept)    5.16      NA       NA    
 8 A      ran_pars Residual  sd__Observation    4.73      NA       NA    
 9 B      fixed    NA        (Intercept)       31.6        1.81    17.5  
10 B      fixed    NA        temperature.L      6.88       1.16     5.93 
# … with 14 more rows
# ℹ Use `print(n = ...)` to see more rows

CodePudding user response：

You can also merge two or more gtsummary tables using the gtsummary::tbl_merge() function. Example below!

library(gtsummary)
#> #StandWithUkraine
library(lme4)
#> Loading required package: Matrix
data(cake)

MxM1 <- 
  plyr::dlply(
    cake,
    "recipe",
    function(x) {
      lmer(angle ~ 1  (1|replicate)  temperature, data=x) %>%
        tbl_regression() %>%
        modify_column_hide(columns = ci) %>%
        modify_column_unhide(columns = std.error)
    }
  )

# Merge all model summaries together with `tbl_merge()` 
tbl <-
MxM1 %>%
  tbl_merge(
    tab_spanner = c("**A**", "**B**", "**C**")
  )

^{Created on 2022-12-17 with reprex v2.0.2}