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fix bugs in package data regeneration for ManyAnalysts manuscript (#148)
* Increment version number to 2.7.5.9000 * bug: fix threshold checking for multivariate model fitting #147 only count `mixed_model` after calling `distinct()` on analysis identifier column and `mixed_model` first. Update argument checking to fail if any id columns not present. docs: update `@details` section about required id column and add `any_of()` to `@importFrom` * docs: rearrange headings in NEWS.md * bug: ensure outlier subset creation occurs on all `exclusion_set` values in Zr #144 removed filter for `exclusion_set` == "complete" * bug: exclude analysis with non-count-based dependent variable from `yi` analysis #145 * feat: #146 add function for excluding extreme estimates based on a multiplier threshold for population parameter estimates * fix typo #146 * docs!: #146 `devtools::document()` * bug: #146 export function * build!: #146 apply exclusion function to Eucalyptus dataset in targets pipeline Note that this functionality was previously included in the manuscript * #146 increment dev version and news before rebuilding package and targets pipeline * - build!: don't forget to filter the corresponding diversity data after exclusions! * - build!: fix #146 regenerate yi data after excluding extreme values * Increment version number to 2.7.6
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,98 @@ | ||
| #' Exclude extreme estimates above a threshold parameter sd | ||
| #' | ||
| #' @param data A dataframe of analyst estimates | ||
| #' @param outcome_variable the name of the variable in `data` containing the analyst estimates | ||
| #' @param outcome_SE variable in `data` containing analyst SE estimates | ||
| #' @param sd_threshold A numeric threshold multiplyer see details | ||
| #' @param param_table A dataframe containing population parameters `mean` and `sd` for each `variable` in a given `dataset` | ||
| #' @param .fn An optional function that will transform parameter estimates to the same scale as `outcome_variable` in `data` | ||
| #' @param ... Arguments supplied to `.fn` | ||
| #' @import dplyr | ||
| #' @importFrom rlang enquo env as_quosures enquos enquo current_env quo_set_env is_null | ||
| #' @importFrom cli cli_h3 cli_alert_success | ||
| #' @importFrom purrr map list_c | ||
| #' @importFrom tidyr pivot_wider hoist | ||
| #' @details | ||
| #' This function is used to exclude extreme estimates from a dataset. The function | ||
| #' calculates a threshold for exclusion based on the mean and standard deviation of | ||
| #' the population parameter estimates in `param_table`. The threshold is calculated | ||
| #' as the mean of the population parameter plus `sd_threshold` times the standard | ||
| #' deviation of the population parameter. Estimates in `data` that are greater than | ||
| #' this threshold are excluded from the output. | ||
| #' | ||
| #' If the user chooses to supply `.fn` and `...` arguments, the function will transform | ||
| #' the population parameter estimates in `param_table` to the same scale as the | ||
| #' `outcome_variable` in `data` using `.fn`, before calculating the threshold for exclusion. | ||
| #' @export | ||
| #' @return A dataframe of analyst estimates with extreme estimates excluded | ||
| #' @examples | ||
| #' # example code | ||
| #' data <- ManyEcoEvo_yi %>% | ||
| #' mutate(data = | ||
| #' map_if(data, | ||
| #' ~ filter(.x, | ||
| #' stringr::str_detect(response_variable_name, | ||
| #' "average.proportion.of.plots.containing", | ||
| #' negate = TRUE)), | ||
| #' .p = dataset == "eucalyptus")) %>% | ||
| #' mutate( | ||
| #' diversity_data = | ||
| #' map2( | ||
| #' .x = diversity_data, | ||
| #' .y = data, | ||
| #' .f = ~ semi_join(.x, .y, join_by(id_col)) %>% | ||
| #' distinct() | ||
| #' ) | ||
| #' ) %>% | ||
| #' prepare_response_variables( | ||
| #' estimate_type = "yi", | ||
| #' param_table = | ||
| #' ManyEcoEvo:::analysis_data_param_tables, | ||
| #' dataset_standardise = "blue tit", | ||
| #' dataset_log_transform = "eucalyptus") %>% | ||
| #' generate_yi_subsets() %>% #TODO: must be run after prepare_response_variables?? | ||
| #' apply_VZ_exclusions( | ||
| #' VZ_colname = list("eucalyptus" = "se_log", | ||
| #' "blue tit" = "VZ"), | ||
| #' VZ_cutoff = 3) %>% | ||
| #' filter(dataset == "eucalyptus", estimate_type == "y25") %>% | ||
| #' pluck("data", 1) | ||
| #' sd_threshold = 3 | ||
| #' param_table <- ManyEcoEvo:::analysis_data_param_tables | ||
| #' exclude_extreme_estimates(data, "mean_log", "se_log", 3, param_table, log_transform, estimate = mean, std.error = sd) | ||
| exclude_extreme_estimates <- function(data, outcome_variable, outcome_SE, sd_threshold = numeric(1L), param_table, .fn = ..., ...) { | ||
| # FOR NOW: allow transformation here, but in future, we make sure that | ||
| # `prepare_response_variables()` returns both `back_transformed_data` and the | ||
| # transformed / standardised data to separate list-columns to retain this data | ||
| # Then downstream functions operate off the list-column `analysis_data` or | ||
| # some other named list-col like `transformed_data` etc. | ||
| dots <- rlang::enquos(...) %>% rlang::as_quosures(env = rlang::env()) | ||
|
|
||
| param_table <- pivot_wider(param_table, names_from = parameter, values_from = value) | ||
|
|
||
| if (!is_null(.fn)){ | ||
| cli::cli_h3("Transforming {.arg param_table} using {.arg .fn}:") | ||
| param_table <- param_table %>% | ||
| rowwise() %>% | ||
| mutate(transformed_values = list(.fn(!!!dots))) %>% | ||
| hoist(transformed_values, | ||
| param_mean = outcome_variable, | ||
| param_sd = outcome_SE) %>% | ||
| select(-transformed_values, -{map(dots, rlang::as_name) %>% list_c()}) | ||
| } else { | ||
| param_table <- param_table %>% | ||
| rename_with(.cols = contains(c("mean", "sd")), ~ paste0("param_", .x)) | ||
| } | ||
| cli::cli_h3("Excluding extreme estimates from data:") | ||
| out <- data %>% | ||
| left_join(param_table, | ||
| by = join_by(response_variable_name == variable)) %>% | ||
| mutate(exclusion_threshold = param_mean + sd_threshold * param_sd) %>% | ||
| filter(if_any(outcome_variable, ~ .x <= exclusion_threshold)) %>% | ||
| select(-starts_with("param_")) | ||
|
|
||
| cli::cli_alert_success("Removed {.val {nrow(data) - nrow(out)}} columns from data with {.arg sd_threshold} = {.val {sd_threshold}}") | ||
|
|
||
| return(out) | ||
| } | ||
|
|
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