Extracting the p-value from an lmerTest model inside a loop function - r

Question

I have a number of linear mixed models, which I have fitted with the lmerTest library, so that the summary() of the function would provide me with p-values of fixed effects.
I have written a loop function that extract the fixed effects of gender:time and gender:time:explanatory variable of interest.
Trying to now also extract the p-value of gender:time fixed effect (step 1) and also gender:time:explanatory variable (step 2).
Normally I can extract the p-value with this code:
coef(summary(model))[,5]["genderfemale:time"]
But inside the loop function it doesn't work and gives the error: "Error in coef(summary(model))[, 5] : subscript out of bounds"
See code
library(lmerTest)
# Create a list of models with interaction terms to loop over
models <- list(
mixed_age_interaction,
mixed_tnfi_year_interaction,
mixed_crp_interaction
)
# Create a list of explanatory variables to loop over
explanatoryVariables <- list(
"age_at_diagnosis",
"bio_drug_start_year",
"crp"
)
loop_function <- function(models, explanatoryVariables) {
# Create an empty data frame to store the results
coef_df <- data.frame(adj_coef_gender_sex = numeric(), coef_interaction_term = numeric(), explanatory_variable = character(), adj_coef_pvalue = numeric())
# Loop over the models and explanatory variables
for (i in seq_along(models)) {
model <- models[[i]]
explanatoryVariable <- explanatoryVariables[[i]]
# Extract the adjusted coefficients for the gender*time interaction
adj_coef <- fixef(model)["genderfemale:time"]
# Extract the fixed effect of the interaction term
interaction_coef <- fixef(model)[paste0("genderfemale:time:", explanatoryVariable)]
# Extract the p-value for the adjusted coefficient for gender*time
adj_coef_pvalue <- coef(summary(model))[,5]["genderfemale:time"]
# Add a row to the data frame with the results for this model
coef_df <- bind_rows(coef_df, data.frame(adj_coef_gender_sex = adj_coef, coef_interaction_term = interaction_coef, explanatory_variable = explanatoryVariable, adj_coef_pvalue = adj_coef_pvalue))
}
return(coef_df)
}
# Loop over the models and extract the fixed effects
coef_df <- loop_function(models, explanatoryVariables)
coef_df
My question is how can I extract the p-values from the models for gender:time and gender:time:explanatory variable and add them to the final data.frame coef_df?
Also adding a summary of one of the models for reference
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: basdai ~ 1 + gender + time + age_at_diagnosis + gender * time +
time * age_at_diagnosis + gender * age_at_diagnosis + gender *
time * age_at_diagnosis + (1 | ID) + (1 | country)
Data: dat
AIC BIC logLik deviance df.resid
254340.9 254431.8 -127159.5 254318.9 28557
Scaled residuals:
Min 1Q Median 3Q Max
-3.3170 -0.6463 -0.0233 0.6092 4.3180
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 154.62 12.434
country (Intercept) 32.44 5.695
Residual 316.74 17.797
Number of obs: 28568, groups: ID, 11207; country, 13
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.669e+01 1.792e+00 2.082e+01 26.048 < 2e-16 ***
genderfemale 2.368e+00 1.308e+00 1.999e+04 1.810 0.0703 .
time -1.451e+01 4.220e-01 2.164e+04 -34.382 < 2e-16 ***
age_at_diagnosis 9.907e-02 2.220e-02 1.963e+04 4.463 8.12e-06 ***
genderfemale:time 1.431e-01 7.391e-01 2.262e+04 0.194 0.8464
time:age_at_diagnosis 8.188e-02 1.172e-02 2.185e+04 6.986 2.90e-12 ***
genderfemale:age_at_diagnosis 8.547e-02 3.453e-02 2.006e+04 2.476 0.0133 *
genderfemale:time:age_at_diagnosis 4.852e-03 1.967e-02 2.274e+04 0.247 0.8052
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) gndrfm time ag_t_d gndrf: tm:g__ gnd:__
genderfemal -0.280
time -0.241 0.331
age_t_dgnss -0.434 0.587 0.511
gendrfml:tm 0.139 -0.519 -0.570 -0.293
tm:g_t_dgns 0.228 -0.313 -0.951 -0.533 0.543
gndrfml:g__ 0.276 -0.953 -0.329 -0.639 0.495 0.343
gndrfml::__ -0.137 0.491 0.567 0.319 -0.954 -0.596 -0.516

Answer 1

The internal function get_coefmat of {lmerTest} might be handy:
if fm is an example {lmer} model ...
library("lmerTest")
fm <- lmer(Informed.liking ~ Gender + Information * Product +
(1 | Consumer) + (1 | Consumer:Product),
data=ham
)
... you can obtain the coefficients including p-values as a dataframe like so (note the triple colon to expose the internal function):
df_coeff <- lmerTest:::get_coefmat(fm) |>
as.data.frame()
output:
## > df_coeff
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.8490289 0.2842897 322.3364 20.5741844 1.173089e-60
## Gender2 -0.2442835 0.2605644 79.0000 -0.9375169 3.513501e-01
## Information2 0.1604938 0.2029095 320.0000 0.7909626 4.295517e-01
## Product2 -0.8271605 0.3453291 339.5123 -2.3952818 1.714885e-02
## Product3 0.1481481 0.3453291 339.5123 0.4290057 6.681912e-01
## ...
edit
Here's a snippet which will return you the extracted coefficents for, e.g., models m1 and m2 as a combined dataframe:
library(dplyr)
library(tidyr)
library(purrr)
library(tibble)
list('m1', 'm2') |> ## observe the quotes
map_dfr( ~ list(
model = .x,
coeff = lmerTest:::get_coefmat(get(.x)) |>
as.data.frame() |>
rownames_to_column()
)
) |>
as_tibble() |>
unnest_wider(coeff)
output:
## + # A tibble: 18 x 7
## model rowname Estimate `Std. Error` df `t value` `Pr(>|t|)`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 m1 (Intercept) 5.85 0.284 322. 20.6 1.17e-60
## 2 m1 Gender2 -0.244 0.261 79.0 -0.938 3.51e- 1
## ...
## 4 m1 Product2 -0.827 0.345 340. -2.40 1.71e- 2
## ...
## 8 m1 Information2:Product3 0.272 0.287 320. 0.946 3.45e- 1
## ...
## 10 m2 (Intercept) 5.85 0.284 322. 20.6 1.17e-60
## 11 m2 Gender2 -0.244 0.261 79.0 -0.938 3.51e- 1
## ...