feat!: delete commented out redundant code #42

index.qmd

@@ -2933,123 +2933,6 @@ MA_yi_summary_stats <-
              .keep = "none")}, 
       by = join_by(dataset, estimate_type)) %>% 
   select(-tidy_mod_summary)
-
-
-# --- old code ----
-
-# 
-# back_transformed_predictions <- 
-#   ManyEcoEvo_yi %>% 
-#   prepare_response_variables_yi(estimate_type = "yi",
-#                                 param_table = ManyEcoEvo:::analysis_data_param_tables) %>% 
-#   generate_yi_subsets()
-# 
-# 
-# raw_mod_data_logged <- 
-#   back_transformed_predictions %>% 
-#   filter(dataset == "eucalyptus") %>%
-#   group_by(estimate_type) %>% 
-#   select(estimate_type, data) %>% 
-#   unnest(data) %>% 
-#   rename(study_id = id_col) %>% 
-#   hoist(params, param_mean = list("value", 1), param_sd = list("value", 2)) %>% 
-#   rowwise() %>% 
-#   mutate(exclusion_threshold = param_mean + 3*param_sd) %>% 
-#   filter(fit < exclusion_threshold) %>% 
-#   mutate(log_vals = map2(fit, se.fit, log_transform, 1000)) %>% 
-#   unnest(log_vals) %>%
-#   select(study_id, 
-#          TeamIdentifier,
-#          estimate_type, 
-#          starts_with("response_"), 
-#          -response_id_S2, 
-#          ends_with("_log")) %>% 
-#   group_by(estimate_type) %>% 
-#   nest()
-# 
-# 
-# mod_data_logged <- raw_mod_data_logged %>% 
-#   mutate(MA_mod = 
-#            map(data, 
-#                ~ ManyEcoEvo::fit_MA_mv(effects_analysis = .x, 
-#                                        Z_colname = mean_log, 
-#                                        VZ_colname = se_log, 
-#                                        estimate_type = "yi")))
-# 
-# 
-# plot_data_logged <- mod_data_logged %>% 
-#   mutate(tidy_mod = map(.x = MA_mod,
-#                         ~broom::tidy(.x,
-#                                      conf.int = TRUE, 
-#                                      include_studies = TRUE) %>% 
-#                           rename(study_id = term))) %>% 
-#   select(tidy_mod) %>% 
-#   unnest(cols = c(tidy_mod)) 
-# 
-# MA_yi_summary_stats <- # ALL ON logged RESPONSE SCALE for EUC, standardized response values for BT
-#   plot_data_logged %>% 
-#   mutate(response_scale = map2(estimate, std.error, log_back, 100)) %>% 
-#   select(estimate_type, study_id, type, response_scale) %>% 
-#   unnest(response_scale) %>% 
-#   rename(estimate = mean_origin, conf.low = lower, conf.high = upper) %>% 
-#   nest(tidy_mod = -estimate_type) %>% 
-#   mutate(dataset = "eucalyptus") %>% 
-#   bind_rows(., {
-#     ManyEcoEvo_yi_results %>% 
-#       ungroup() %>% 
-#       filter(exclusion_set == "complete", dataset == "blue tit") %>% 
-#       select(dataset, estimate_type, MA_mod, effects_analysis, -exclusion_set) %>% 
-#       group_by(estimate_type, dataset) %>% 
-#       transmute(tidy_mod = map(.x = MA_mod,
-#                                ~broom::tidy(.x,
-#                                             conf.int = TRUE, 
-#                                             include_studies = TRUE) %>% 
-#                                  rename(study_id = term)))
-#   }) %>% 
-#   mutate(MA_mean = map(tidy_mod, filter, type == "summary")) %>% 
-#   hoist(MA_mean, 
-#         mean = "estimate", 
-#         MA_conf.low = "conf.low", 
-#         MA_conf.high = "conf.high") %>% 
-#   mutate(max_min_est = map(tidy_mod, 
-#                            ~ filter(.x, type == "study") %>%
-#                              summarise(max_est = max(estimate),
-#                                        min_est = min(estimate)))) %>% 
-#   mutate(max_min_CI = map(tidy_mod, 
-#                           ~ filter(.x, type == "study") %>%
-#                             summarise(max_upper_CI = max(conf.high),
-#                                       min_lower_CI = min(conf.low)))) %>% 
-#   unnest_wider(col = c(max_min_est, max_min_CI)) %>% 
-#   ungroup %>% 
-#   rows_update({plot_data_logged %>% #hells yes to this gem of a function!
-#       mutate(dataset = "eucalyptus") %>% 
-#       filter(type != "summary") %>% 
-#       nest(tidy_mod = c(-estimate_type, -dataset))}, 
-#       by = c("dataset", "estimate_type")) %>% 
-#   mutate(no_effect = 
-#            map_int(tidy_mod, 
-#                    ~ filter(.x, 
-#                             estimate >0 & conf.low <= 0 | estimate <0 & conf.high >= 0, 
-#                             type == "study") %>% 
-#                      nrow() ),
-#          pos_sign = 
-#            map_int(tidy_mod, 
-#                    ~ filter(.x, estimate >0, conf.low > 0, 
-#                             type == "study") %>% 
-#                      nrow()),
-#          neg_sign = 
-#            map_int(tidy_mod, 
-#                    ~ filter(.x, estimate < 0, conf.high < 0, 
-#                             type == "study") %>% 
-#                      nrow()),
-#          total_effects = 
-#            map_int(tidy_mod,
-#                    ~ filter(.x, 
-#                             type == "study") %>% 
-#                      nrow()
-#            )) %>% 
-#   select(-tidy_mod, -MA_mean) %>% 
-#   rename(MA_mean = mean)
 ```
 
 As with the effect size $Z_r$, we observed substantial variability in the size of out-of-sample predictions derived from the analysts' models.