#* model_tests_covid_pvals:
#*   attr:
#*     fillcolor: '7'
#*   desc: Runs multiple test correction on all the model results. For models that diverged,
#*     resulting in nonsense odds ratio estimates, the p-values are replaced by NA prior
#*     to correction.
#*   ext: R
#*   inputs:
#*   - model_tests_covid_gapplyCollect
#* 

library(dplyr)

model_tests_covid_pvals <- function( model_tests_covid_gapplyCollect) {
    df <- model_tests_covid_gapplyCollect

    # some topics the model diverged (small groups being fully seperable)
    # we don't want to drop them altogether (or we'd lose the facet in later plots), just
    # replace entries with NA so the lines/points aren't plotted
    failed_convergence_topics <- df %>% 
                                 filter(abs(estimate) > 1000) %>%
                                 pull(topic_name) %>%
                                 unique()
    
    df <- df %>%
        mutate(p_value = ifelse(topic_name %in% failed_convergence_topics, NA, p_value)) %>%
        mutate(estimate = ifelse(topic_name %in% failed_convergence_topics, NA, estimate)) %>%
        mutate(asymp_LCL = ifelse(topic_name %in% failed_convergence_topics, NA, asymp_LCL)) %>%
        mutate(asymp_UCL = ifelse(topic_name %in% failed_convergence_topics, NA, asymp_UCL)) %>%

        # some of the p-values are now NA, and the correction handles NA's by... help(p.adjust)
        mutate(p_adj = p.adjust(p_value, method = "holm")) %>%
        mutate(p_adj_small = ifelse(p_adj < 0.05, TRUE, FALSE))

    return(df)
}