diagnostics_table

diagnostics_table <- function(model_list) {

# Entropy
entropy <- c(model_list$summaries$Entropy, rep(NA, model_list$summaries$NLatentClasses-1))

# 95% k-Class and k-class 95% Confidence Intervals
k_ci <- model_list$parameters$ci.unstandardized %>% 
  filter(paramHeader == "New.Additional.Parameters") %>% 
  unite(CI, c(low2.5,up2.5), sep=", ", remove = TRUE) %>% 
  mutate(CI = paste0("[", CI, "]")) %>% 
  rename(kclass=est) %>% 
  dplyr::select(kclass, CI)

# AvePPk = Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column)
avePPk <- tibble(avePPk = diag(model_list$class_counts$avgProbs.mostLikely))

# mcaPk = modal class assignment proportion 
mcaPk <- round(model_list$class_counts$mostLikely,3) %>% 
  mutate(model = paste0("Class ", class)) %>% 
  add_column(avePPk, k_ci) %>% 
  rename(mcaPk = proportion) %>% 
  dplyr::select(model, kclass, CI, mcaPk, avePPk)

# OCCk = odds of correct classification
OCCk <- mcaPk %>% 
  mutate(OCCk = round((avePPk/(1-avePPk))/(kclass/(1-kclass)),3))

# Put everything together
class_df <- data.frame(OCCk)


class_table <- class_df %>% 
  gt() %>%
    tab_header(
    title = "Model Classification Diagnostics") %>%
    cols_label(
      model = md("*k*-Class"),
      kclass = md("*k*-Class Proportions"),
      CI = "95% CI",
      mcaPk = md("*mcaPk*"),
      avePPk = md("*AvePPk*"),
      OCCk = md("*OCCk*")) %>% 
    sub_missing(6,
              missing_text = "") %>%
  cols_align(align = "center") %>% 
  opt_align_table_header(align = "left") %>% 
  gt::tab_options(table.font.names = "Times New Roman")

class_table

}