Trying to use tidy for a power analysis and using clmm2 - r

Question

I'm trying to do a power analysis on a clmm2 analysis that I'm doing.
This is the code for the particular statistical model:
test <- clmm2(risk_sensitivity ~ treat + sex + dispersal +
sex*dispersal + treat*dispersal + treat*sex,random = id, data = datasocial, Hess=TRUE)
Now, I have the following function:
sim_experiment_power <- function(rep) {
s <- sim_experiment(n_sample = 1000,
prop_disp = 0.10,
prop_fem = 0.35,
disp_probability = 0.75,
nondisp_probability = 0.90,
fem_probability = 0.75,
mal_probability = 0.90)
broom.mixed::tidy(s) %>%
mutate(rep = rep)
}
my_power <- map_df(1:10, sim_experiment_power)
The details of the function sim_experiment are not relevant because they are working as expected. The important thing to know is that it spits up a statistical clmm2 result. My objective with the function above is to do a power analysis. However, I get the following error:
Error: No tidy method for objects of class clmm2
I'm a bit new to R, but I guess it means that tidy doesn't work with clmm2. Does anyone know a work-around for this issue?
EDIT: This is what follows the code that I posted above, which is ultimately what I'm trying to get.
You can then plot the distribution of estimates across your simulations.
ggplot(my_power, aes(estimate, color = term)) +
geom_density() +
facet_wrap(~term, scales = "free")
You can also just calculate power as the proportion of p-values less than your alpha.
my_power %>%
group_by(term) %>%
summarise(power <- mean(p.value < 0.05))

Answer 1

For what you need, you can write a function to return the coefficients with the same column name:
library(ordinal)
library(dplyr)
library(purrr)
tidy_output_clmm = function(fit){
results = as.data.frame(coefficients(summary(fit)))
colnames(results) = c("estimate","std.error","statistic","p.value")
results %>% tibble::rownames_to_column("term")
}
Then we apply it using an example where I sample the wine dataset in ordinal:
sim_experiment_power <- function(rep) {
idx = sample(nrow(wine),replace=TRUE)
s <- clmm2(rating ~ temp, random=judge, data=wine[idx,], nAGQ=10,Hess=TRUE)
tidy_output_clmm(s) %>% mutate(rep=rep)
}
my_power <- map_df(1:10, sim_experiment_power)
Plotting works:
ggplot(my_power, aes(estimate, color = term)) +
geom_density() +
facet_wrap(~term, scales = "free")
And so does power:
my_power %>% group_by(term) %>% summarise(power = mean(p.value < 0.05))
# A tibble: 5 x 2
term power
<chr> <dbl>
1 1|2 0.9
2 2|3 0.1
3 3|4 1
4 4|5 1
5 tempwarm 1