FDR correction - extracting p-values from lmer() and creating vectors for use in p.adjust in R

Question

I am trying to do FDR correction for some region of interest neuroimaging data. I have run 18 linear mixed effects models overall and I have made sure that the order of the coefficients in the output would be the same in each model.

I have saved the output from each model in the following:

tidy_model1 <-tidy(model1)
tidy_model2 <-tidy(model2)
....
tidy_model18 <-tidy(model18)

I am now trying to make my life easier and create a loop which goes over a list with the names of the above model objects and creates a vector of p-values for each coefficient which I will then enter in the p.adjust function to retrieve the adjusted p-values.

so I create a list:

model_list <- list(tidy_model1,
tidy_model2,... tidy_model18)

I have tried the following loops:

for (i in 1:18) {
model_list[i] %>%
variable1_pval <- p.value[1]
}

and

for (i in 1:18) {
variable1_pval <- model_list[i]$p.value[1]
}

So the above should give me a vector of p-values for coefficient 1 of the model.

However, I get a null vector in both cases.

I know I am not providing my data but any suggestion as to why these loops might not be working are welcome!

Thank you

Answer 1

I made up a list of models:

library(nlme)
library(broom)

models <- lapply(1:5,function(i){
idx= sample(nrow(Orthodont),replace=TRUE)
lme(distance ~ age, random=~Sex,data = Orthodont[idx,])
})

model_list <- lapply(models,tidy,effects="fixed")

In these models, the useful coefficient is the second:

model_list[[1]]
# A tibble: 2 x 5
  term        estimate std.error statistic  p.value
  <chr>          <dbl>     <dbl>     <dbl>    <dbl>
1 (Intercept)   15.9      1.03       15.5  7.77e-26
2 age            0.739    0.0871      8.48 9.13e-13

You can obtain the p-values in a vector like this, for your example use p.value1:

sapply(model_list,function(x)x$p.value[2])

A better way to keep track of your models, and not populate the environment with variables, is to use purrr, dplyr (see more here) :

library(purrr)
library(dplyr)

models = tibble(name=1:5,models=models) %>%
mutate(tidy_res = map(models,tidy,effects="fixed"))

models

# A tibble: 5 x 3
   name models tidy_res        
  <int> <list> <list>          
1     1 <lme>  <tibble [2 × 5]>
2     2 <lme>  <tibble [2 × 5]>
3     3 <lme>  <tibble [2 × 5]>
4     4 <lme>  <tibble [2 × 5]>
5     5 <lme>  <tibble [2 × 5]>

models %>% unnest(tidy_res) %>% filter(term=="age")
# A tibble: 5 x 7
   name models term  estimate std.error statistic  p.value
  <int> <list> <chr>    <dbl>     <dbl>     <dbl>    <dbl>
1     1 <lme>  age      0.587    0.0601      9.77 2.44e-15
2     2 <lme>  age      0.677    0.0663     10.2  3.91e-16
3     3 <lme>  age      0.588    0.0603      9.74 3.05e-15
4     4 <lme>  age      0.653    0.0529     12.3  2.74e-20
5     5 <lme>  age      0.638    0.0623     10.2  3.34e-16