r combine results from multiple lme4 objects - r

Question

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.

Answer 1

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

Answer 2

Update:
While the answer by #Daniel D. Sjoberg is perfect and the desired one. Here is the answer to OP's question in the comments:
"How can i convert the final results from long format to wide, by each recipe?"
After filtering temperature we could use pivot_wider and some tweaking thereafter:
Note we have to use broom.mixed package for our lmer
library(lme4)
library(tidyverse)
#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$recipe), .before = 1)
}) %>%
filter(str_detect(term, "temperature")) %>%
select(recipe, term, Beta=estimate, SE = std.error) %>%
pivot_wider(names_from = recipe,
values_from = c(Beta, SE)) %>%
rename_with(~ str_replace(., "(.*)_(.*)", "\\2_\\1"), -1) %>%
select(term, sort(colnames(.)))
term A_Beta A_SE B_Beta B_SE C_Beta C_SE
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 temperature.L 6.43 1.22 6.88 1.16 6.52 1.12
2 temperature.Q -0.713 1.22 -0.946 1.16 0.502 1.12
3 temperature.C -2.33 1.22 0.368 1.16 0.313 1.12
4 temperature^4 -3.35 1.22 -0.328 1.16 -0.214 1.12
5 temperature^5 -0.151 1.22 -0.815 1.16 -1.78 1.12
First answer:
You need something like this?:
library(lme4)
data(cake)
library(dplyr)
library(broom)
library(broom.mixed)
cake %>%
mutate(recipe = as_factor(recipe)) %>%
group_by(recipe) %>%
group_split() %>%
map_dfr(.f = function(df){
lmer(angle ~ 1 + (1|replicate) + temperature,
data=df) %>%
tidy() %>%
add_column(recipe = unique(df$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