Collinearity subset analysis does not subset correct list-column of df's #40

egouldo · 2024-06-14T00:13:33Z

list-col effects_analysis is not being subset, data is. Function is applied after other pre-processing to make ManyEcoEvo::ManyEcoEvo_results. Downstream analyses use effects_analysis as the input list-col of df's, however.

library(ManyEcoEvo)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

library(purrr)

pull_df <- function(x,y){
  x %>% 
    filter(dataset == "blue tit", 
           publishable_subset == "All", 
           expertise_subset == "All", 
           exclusion_set == "complete") %>% 
    pull({{y}})
}

ManyEcoEvo::ManyEcoEvo_results %>% pull_df(data) %>% map(dim)
#> $subset_complete
#> [1] 131  40
#> 
#> $subset_complete
#> [1] 119  40

ManyEcoEvo::ManyEcoEvo_results %>% pull_df(effects_analysis) %>% map(dim)
#> [[1]]
#> [1] 131  48
#> 
#> [[2]]
#> [1] 131  48

^{Created on 2024-06-14 with reprex v2.1.0}

ManyEcoEvo/R/generate_collinearity_subset.R

Lines 53 to 55 in 77c89f6

    
           mutate(data = map(.x = data,  
        
                             .f = dplyr::anti_join, collinearity_subset,   
        
                             by = join_by(response_id, id_col, dataset) )) %>%

The text was updated successfully, but these errors were encountered:

egouldo · 2024-06-14T00:56:08Z

But when I try to tar_make() expert_subset is not present yet and throwing error... so maybe it's downstream subsetting causing the problem??

library(ManyEcoEvo)
library(tidyverse)

a <- 
  ManyEcoEvo %>% 
  prepare_response_variables(estimate_type = "Zr") |>  
  generate_exclusion_subsets(estimate_type = "Zr") |> 
  generate_rating_subsets() |> 
  generate_expertise_subsets(ManyEcoEvo:::expert_subset)
#> 
#> ── Applying exclusion rules and generating exclusion subsets ───────────────────
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 484.0193.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 666.56874.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 590.18263.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.006225,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.003996,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 481.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.09247,
#> 3. adjusted_df 316.17526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.029,
#> 3. adjusted_df 366.3.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.042,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01416305,
#> 3. adjusted_df 257.905.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.030382264,
#> 3. adjusted_df 2372.82.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01409,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.008781,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.014853,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.000769,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.033978443,
#> 3. adjusted_df 347.4992526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01823188,
#> 3. adjusted_df 55.44391.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02039768,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02496,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.03575,
#> 3. adjusted_df NA.
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.29212,
#> 3. adjusted_df 21.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007831,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.07216,
#> 3. adjusted_df 0.560867697.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.57,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.2328,
#> 3. adjusted_df 343.24787.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.3188723,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0059286,
#> 3. adjusted_df 1.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007385,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.052462,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 8.98,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 7.97,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.19,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 18.5,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.92,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.529,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 2.89,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01312667,
#> 3. adjusted_df -2.6269353.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.197,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.192,
#> 3. adjusted_df 82.703.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.1,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0048042,
#> 3. adjusted_df 341.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.21,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.5756016,
#> 3. adjusted_df 3.536992.
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(response_id)`
#> Joining with `by = join_by(response_id)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`

a %>% generate_collinearity_subset(ManyEcoEvo:::collinearity_subset)
#> Error in `mutate()`:
#> ℹ In argument: `effects_analysis = map(...)`.
#> ℹ In group 1: `dataset = "blue tit"`, `exclusion_set = "complete"`,
#>   `estimate_type = "Zr"`.
#> Caused by error:
#> ! object 'effects_analysis' not found

^{Created on 2024-06-14 with reprex v2.1.0}

egouldo · 2024-06-14T01:01:21Z

Rebuilt pkg after f242965 and reran reprex, passes now, so def a downstream issue from generate_collinerity_subset()

library(ManyEcoEvo)
library(tidyverse)

a <- 
  ManyEcoEvo %>% 
  prepare_response_variables(estimate_type = "Zr") |>  
  generate_exclusion_subsets(estimate_type = "Zr") |> 
  generate_rating_subsets() |> 
  generate_expertise_subsets(ManyEcoEvo:::expert_subset)
#> 
#> ── Applying exclusion rules and generating exclusion subsets ───────────────────
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 484.0193.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 666.56874.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 590.18263.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.006225,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.003996,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 481.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.09247,
#> 3. adjusted_df 316.17526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.029,
#> 3. adjusted_df 366.3.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.042,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01416305,
#> 3. adjusted_df 257.905.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.030382264,
#> 3. adjusted_df 2372.82.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01409,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.008781,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.014853,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.000769,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.033978443,
#> 3. adjusted_df 347.4992526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01823188,
#> 3. adjusted_df 55.44391.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02039768,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02496,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.03575,
#> 3. adjusted_df NA.
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.29212,
#> 3. adjusted_df 21.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007831,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.07216,
#> 3. adjusted_df 0.560867697.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.57,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.2328,
#> 3. adjusted_df 343.24787.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.3188723,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0059286,
#> 3. adjusted_df 1.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007385,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.052462,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 8.98,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 7.97,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.19,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 18.5,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.92,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.529,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 2.89,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01312667,
#> 3. adjusted_df -2.6269353.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.197,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.192,
#> 3. adjusted_df 82.703.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.1,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0048042,
#> 3. adjusted_df 341.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.21,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.5756016,
#> 3. adjusted_df 3.536992.
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(id_col)`
#> Joining with `by = join_by(response_id)`
#> Joining with `by = join_by(response_id)`
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`

a %>% generate_collinearity_subset(ManyEcoEvo:::collinearity_subset)
#> # A tibble: 15 × 8
#> # Groups:   dataset, exclusion_set, estimate_type [4]
#>    dataset    exclusion_set estimate_type data                diversity_data    
#>    <chr>      <chr>         <chr>         <named list>        <named list>      
#>  1 blue tit   complete      Zr            <tibble [131 × 40]> <tibble>          
#>  2 eucalyptus complete      Zr            <tibble [79 × 40]>  <tibble [79 × 61]>
#>  3 blue tit   partial       Zr            <tibble [118 × 40]> <tibble>          
#>  4 eucalyptus partial       Zr            <tibble [70 × 40]>  <tibble [70 × 61]>
#>  5 blue tit   complete      Zr            <tibble [109 × 6]>  <tibble>          
#>  6 blue tit   complete      Zr            <tibble [32 × 6]>   <tibble [32 × 54]>
#>  7 eucalyptus complete      Zr            <tibble [55 × 6]>   <tibble [55 × 61]>
#>  8 eucalyptus complete      Zr            <tibble [13 × 6]>   <tibble [13 × 61]>
#>  9 blue tit   partial       Zr            <tibble [100 × 6]>  <tibble>          
#> 10 blue tit   partial       Zr            <tibble [32 × 6]>   <tibble [32 × 54]>
#> 11 eucalyptus partial       Zr            <tibble [52 × 6]>   <tibble [52 × 61]>
#> 12 eucalyptus partial       Zr            <tibble [10 × 6]>   <tibble [10 × 61]>
#> 13 blue tit   complete      Zr            <tibble [89 × 40]>  <tibble [89 × 54]>
#> 14 eucalyptus complete      Zr            <tibble [34 × 40]>  <tibble [34 × 61]>
#> 15 blue tit   complete      Zr            <tibble [117 × 40]> <tibble>          
#> # ℹ 3 more variables: publishable_subset <chr>, expertise_subset <chr>,
#> #   collinearity_subset <chr>

^{Created on 2024-06-14 with reprex v2.1.0}

egouldo · 2024-06-14T01:10:19Z

OK, error is coming from meta_analyse_datasets()

library(ManyEcoEvo)
library(tidyverse)

pull_df <- function(x,y){
  x %>% 
    filter(dataset == "blue tit", 
           publishable_subset == "All", 
           expertise_subset == "All", 
           exclusion_set == "complete") %>% 
    pull({{y}})
}

a <- 
  ManyEcoEvo %>% 
  prepare_response_variables(estimate_type = "Zr") |>  
  generate_exclusion_subsets(estimate_type = "Zr") |> 
  generate_rating_subsets() |> 
  generate_expertise_subsets(ManyEcoEvo:::expert_subset) %>% 
  generate_collinearity_subset(ManyEcoEvo:::collinearity_subset)
#> 
#> ── Applying exclusion rules and generating exclusion subsets ───────────────────
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 484.0193.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 666.56874.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 590.18263.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.006225,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.003996,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 481.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.09247,
#> 3. adjusted_df 316.17526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.029,
#> 3. adjusted_df 366.3.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.042,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01416305,
#> 3. adjusted_df 257.905.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.030382264,
#> 3. adjusted_df 2372.82.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01409,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.008781,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.014853,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.000769,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.033978443,
#> 3. adjusted_df 347.4992526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01823188,
#> 3. adjusted_df 55.44391.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02039768,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02496,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.03575,
#> 3. adjusted_df NA.
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.29212,
#> 3. adjusted_df 21.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007831,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.07216,
#> 3. adjusted_df 0.560867697.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.57,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.2328,
#> 3. adjusted_df 343.24787.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.3188723,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0059286,
#> 3. adjusted_df 1.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007385,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.052462,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 8.98,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 7.97,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.19,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 18.5,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.92,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.529,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 2.89,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01312667,
#> 3. adjusted_df -2.6269353.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.197,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.192,
#> 3. adjusted_df 82.703.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.1,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0048042,
#> 3. adjusted_df 341.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.21,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.5756016,
#> 3. adjusted_df 3.536992.
#> Joining with `by = join_by(id_col, dataset)`
#> Joining with `by = join_by(id_col, dataset)`
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#> Joining with `by = join_by(id_col, dataset)`
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b <- 
  a %>% 
  compute_MA_inputs(estimate_type = "Zr") 
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#> Joining with `by = join_by(id_col)`

b %>% 
  pull_df(data) %>% map(dim)
#> $subset_complete
#> [1] 131  40
#> 
#> $subset_complete
#> [1] 117  40

b %>% 
  pull_df(effects_analysis) %>% map(dim)
#> $subset_complete
#> [1] 131  42
#> 
#> $subset_complete
#> [1] 117  42

c <- b %>%   generate_outlier_subsets()
#> Joining with `by = join_by(id_col, dataset)`
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c %>% 
  pull_df(data) %>% map(dim)
#> $subset_complete
#> [1] 131  40
#> 
#> $subset_complete
#> [1] 117  40

c %>% 
  pull_df(effects_analysis) %>% map(dim)
#> $subset_complete
#> [1] 131  42
#> 
#> $subset_complete
#> [1] 117  42

d <- c %>% 
  filter(expertise_subset != "expert" | exclusion_set != "complete-rm_outliers") |> #TODO mv into generate_outlier_subsets() so aren't created in the first place
  meta_analyse_datasets()
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> Registered S3 method overwritten by 'lava':
#>   method         from    
#>   print.estimate EnvStats
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.2731 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.5126 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.2093 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.5529 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.0153 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6351 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.7593 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 1.0478 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.1757 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.154 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.0153 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6344 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.8497 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 1.0478 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.1373 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.3943 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.5842 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.4433 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.4887 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompleteAllAll
#> Registered S3 method overwritten by 'parsnip':
#>   method          from 
#>   print.nullmodel vegan
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompleteAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompleteAllexpert
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompletedata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompletedata_flawed_majorAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcomplete-rm_outliersAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcomplete-rm_outliersdata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcomplete-rm_outliersdata_flawed_majorAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartialAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartialdata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartialdata_flawed_majorAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartial-rm_outliersAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartial-rm_outliersdata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartial-rm_outliersdata_flawed_majorAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ ZreucalyptuscompleteAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ ZreucalyptuscompleteAllexpert
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscompletedata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscompletedata_flawed_majorAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscomplete-rm_outliersAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscomplete-rm_outliersdata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscomplete-rm_outliersdata_flawed_majorAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ ZreucalyptuspartialAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartialdata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartialdata_flawed_majorAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartial-rm_outliersAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartial-rm_outliersdata_flawedAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartial-rm_outliersdata_flawed_majorAll
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> Warning: There were 61 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> ℹ In group 1: `estimate_type = "Zr"`, `dataset = "blue tit"`, `exclusion_set =
#>   "complete"`, `publishable_subset = "All"`, `expertise_subset = "All"`.
#> Caused by warning in `optwrap()`:
#> ! convergence code -4 from nloptwrap: NLOPT_ROUNDOFF_LIMITED: Roundoff errors led to a breakdown of the optimization algorithm. In this case, the returned minimum may still be useful. (e.g. this error occurs in NEWUOA if one tries to achieve a tolerance too close to machine precision.)
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 60 remaining warnings.

d %>% pull_df(data) %>% map(dim)
#> $subset_complete
#> [1] 131  40
#> 
#> $subset_complete
#> [1] 117  40

d %>% pull_df(effects_analysis) %>% map(dim)
#> [[1]]
#> [1] 131  48
#> 
#> [[2]]
#> [1] 131  48

^{Created on 2024-06-14 with reprex v2.1.0}

- update roxygen - devtools::document() - rebuild pkg

egouldo · 2024-06-14T02:02:15Z

65d4fd2 fixes issue:

library(ManyEcoEvo)
#> Warning: replacing previous import 'purrr::%@%' by 'rlang::%@%' when loading
#> 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::flatten_lgl' by 'rlang::flatten_lgl'
#> when loading 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::splice' by 'rlang::splice' when
#> loading 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::flatten_chr' by 'rlang::flatten_chr'
#> when loading 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::flatten_raw' by 'rlang::flatten_raw'
#> when loading 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::flatten' by 'rlang::flatten' when
#> loading 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::flatten_dbl' by 'rlang::flatten_dbl'
#> when loading 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::invoke' by 'rlang::invoke' when
#> loading 'ManyEcoEvo'
#> Warning: replacing previous import 'purrr::flatten_int' by 'rlang::flatten_int'
#> when loading 'ManyEcoEvo'

library(tidyverse)

pull_df <- function(x,y){
  x %>% 
    filter(dataset == "blue tit", 
           publishable_subset == "All", 
           expertise_subset == "All", 
           exclusion_set == "complete") %>% 
    pull({{y}})
}

a <- 
  ManyEcoEvo %>% 
  prepare_response_variables(estimate_type = "Zr") |>  
  generate_exclusion_subsets(estimate_type = "Zr") |> 
  generate_rating_subsets() |> 
  generate_expertise_subsets(ManyEcoEvo:::expert_subset) %>% 
  generate_collinearity_subset(ManyEcoEvo:::collinearity_subset)
#> 
#> ── Applying exclusion rules and generating exclusion subsets ───────────────────
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 484.0193.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 666.56874.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 590.18263.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.006225,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.003996,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 481.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.09247,
#> 3. adjusted_df 316.17526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.029,
#> 3. adjusted_df 366.3.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.042,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01416305,
#> 3. adjusted_df 257.905.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.030382264,
#> 3. adjusted_df 2372.82.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01409,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.008781,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.014853,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.000769,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.033978443,
#> 3. adjusted_df 347.4992526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01823188,
#> 3. adjusted_df 55.44391.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02039768,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02496,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.03575,
#> 3. adjusted_df NA.
#> 
#> ── Computing meta-analysis inputsfor estimate type Zr ──────────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.29212,
#> 3. adjusted_df 21.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007831,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.07216,
#> 3. adjusted_df 0.560867697.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.57,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.2328,
#> 3. adjusted_df 343.24787.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.3188723,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0059286,
#> 3. adjusted_df 1.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007385,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.052462,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 8.98,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 7.97,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.19,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 18.5,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.92,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.529,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 2.89,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01312667,
#> 3. adjusted_df -2.6269353.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.197,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.192,
#> 3. adjusted_df 82.703.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.1,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0048042,
#> 3. adjusted_df 341.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.21,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.5756016,
#> 3. adjusted_df 3.536992.
#> Joining with `by = join_by(id_col, dataset)`
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b <- 
  a %>% 
  compute_MA_inputs(estimate_type = "Zr") 
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b %>% 
  pull_df(data) %>% map(dim)
#> $subset_complete
#> [1] 131  40
#> 
#> $subset_complete
#> [1] 117  40

b %>% 
  pull_df(effects_analysis) %>% map(dim)
#> $subset_complete
#> [1] 131  42
#> 
#> $subset_complete
#> [1] 117  42

c <- b %>%   generate_outlier_subsets()
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c %>% 
  pull_df(data) %>% map(dim)
#> $subset_complete
#> [1] 131  40
#> 
#> $subset_complete
#> [1] 117  40

c %>% 
  pull_df(effects_analysis) %>% map(dim)
#> $subset_complete
#> [1] 131  42
#> 
#> $subset_complete
#> [1] 117  42

d <- c %>% 
  filter(expertise_subset != "expert" | exclusion_set != "complete-rm_outliers") |> #TODO mv into generate_outlier_subsets() so aren't created in the first place
  meta_analyse_datasets()
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Fitting multivariate metaregression ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Calculating absolute deviation scores from standardised effect sizes ──
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> Registered S3 method overwritten by 'lava':
#>   method         from    
#>   print.estimate EnvStats
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.2731 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.5126 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.2093 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.5529 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.0153 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6351 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.7593 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 1.0478 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.1757 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.154 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.0153 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6344 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.8497 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for blue tit ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 1.0478 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.1373 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.3943 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.5842 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1e-04 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.4433 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for eucalyptus ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0.4887 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompleteAllAllAll
#> Registered S3 method overwritten by 'parsnip':
#>   method          from 
#>   print.nullmodel vegan
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompleteAllAllcollinearity_removed
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompleteAllexpertAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompletedata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcompletedata_flawed_majorAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcomplete-rm_outliersAllAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcomplete-rm_outliersdata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titcomplete-rm_outliersdata_flawed_majorAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartialAllAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartialdata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartialdata_flawed_majorAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartial-rm_outliersAllAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartial-rm_outliersdata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zrblue titpartial-rm_outliersdata_flawed_majorAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ ZreucalyptuscompleteAllAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ ZreucalyptuscompleteAllexpertAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscompletedata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscompletedata_flawed_majorAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscomplete-rm_outliersAllAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscomplete-rm_outliersdata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuscomplete-rm_outliersdata_flawed_majorAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ ZreucalyptuspartialAllAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartialdata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartialdata_flawed_majorAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartial-rm_outliersAllAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartial-rm_outliersdata_flawedAllAll
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ Zreucalyptuspartial-rm_outliersdata_flawed_majorAllAll
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting metaregression with continuous ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting lmer with categorical ratings predictor on box_cox_transformed outcomes ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> Warning: There were 62 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> ℹ In group 1: `estimate_type = "Zr"`, `dataset = "blue tit"`, `exclusion_set =
#>   "complete"`, `publishable_subset = "All"`, `expertise_subset = "All"`,
#>   `collinearity_subset = "All"`.
#> Caused by warning in `optwrap()`:
#> ! convergence code -4 from nloptwrap: NLOPT_ROUNDOFF_LIMITED: Roundoff errors led to a breakdown of the optimization algorithm. In this case, the returned minimum may still be useful. (e.g. this error occurs in NEWUOA if one tries to achieve a tolerance too close to machine precision.)
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 61 remaining warnings.

d %>% pull_df(data) %>% map(dim)
#> $subset_complete
#> [1] 131  40
#> 
#> $subset_complete
#> [1] 117  40

d %>% pull_df(effects_analysis) %>% map(dim)
#> [[1]]
#> [1] 131  48
#> 
#> [[2]]
#> [1] 117  48

^{Created on 2024-06-14 with reprex v2.1.0}

- rerun tar_make() - rebuild package with updated data

egouldo · 2024-07-29T10:44:33Z

From #29 noticed issues where pipeline functions are missing a level of grouping for applying Zr functions! (i.e. group_by() calls are missing collinearity_subset !

…s` data is lumped together.

egouldo · 2024-08-10T11:28:10Z

Potentially related to #75 ??

egouldo · 2024-08-28T16:28:01Z

Double check after updates on #121

egouldo · 2024-09-10T04:56:45Z

Issue is resolved:

Local .Rprofile detected at /Users/elliotgould/Documents/GitHub/ManyEcoEvo/.Rprofile

library(tidyverse)

dim_data <-
  targets::tar_read(ManyEcoEvo_results) %>%
  select(ends_with("set")) %>%
  bind_cols(targets::tar_read(ManyEcoEvo_results) %>% pull(data) %>% map_dfr(~ dim(.x) %>%
    set_names(c("rows", "cols")) %>%
    as_tibble_row()))
#> Registered S3 method overwritten by 'parsnip':
#>   method          from 
#>   print.nullmodel vegan
#> Registered S3 method overwritten by 'lava':
#>   method         from    
#>   print.estimate EnvStats

dim_effects_analysis <-
  targets::tar_read(ManyEcoEvo_results) %>%
  select(ends_with("set")) %>%
  bind_cols(targets::tar_read(ManyEcoEvo_results) %>% pull(effects_analysis) %>% map_dfr(~ dim(.x) %>%
    set_names(c("rows", "cols")) %>%
    as_tibble_row()))

left_join(dim_data, dim_effects_analysis, by = join_by(exclusion_set, dataset, publishable_subset, expertise_subset, collinearity_subset)) %>%
  select(-contains("cols")) %>%
  mutate(difference = rows.x == rows.y) %>%
  filter(difference) %>%
  knitr::kable()

exclusion_set	dataset	publishable_subset	expertise_subset	collinearity_subset	rows.x	rows.y	difference
complete	blue tit	All	All	All	131	131	TRUE
complete	eucalyptus	All	All	All	79	79	TRUE
partial	blue tit	All	All	All	118	118	TRUE
partial	eucalyptus	All	All	All	70	70	TRUE
complete	blue tit	data_flawed	All	All	109	109	TRUE
complete	blue tit	data_flawed_major	All	All	32	32	TRUE
complete	eucalyptus	data_flawed	All	All	55	55	TRUE
complete	eucalyptus	data_flawed_major	All	All	13	13	TRUE
partial	blue tit	data_flawed	All	All	100	100	TRUE
partial	blue tit	data_flawed_major	All	All	32	32	TRUE
partial	eucalyptus	data_flawed	All	All	52	52	TRUE
partial	eucalyptus	data_flawed_major	All	All	10	10	TRUE
complete	blue tit	All	expert	All	89	89	TRUE
complete	eucalyptus	All	expert	All	34	34	TRUE
complete	blue tit	All	All	collinearity_removed	117	117	TRUE
complete-rm_outliers	blue tit	All	All	All	127	127	TRUE
complete-rm_outliers	eucalyptus	All	All	All	75	75	TRUE
partial-rm_outliers	blue tit	All	All	All	114	114	TRUE
partial-rm_outliers	eucalyptus	All	All	All	66	66	TRUE

^{Created on 2024-09-10 with reprex v2.1.0}

egouldo added the bug an unexpected problem or unintended behavior label Jun 14, 2024

egouldo added this to the respond-re milestone Jun 14, 2024

egouldo self-assigned this Jun 14, 2024

egouldo added a commit that referenced this issue Jun 14, 2024

#40 update fn to correctly subset for collinearity analysis

3e00769

egouldo added a commit that referenced this issue Jun 14, 2024

#40 update roxygen and generate man

247771c

egouldo added a commit that referenced this issue Jun 14, 2024

#40 rm incorrect effects_analysis subsetting

f242965

egouldo referenced this issue Jun 14, 2024

fix #41 ensure collinearity data is properly subset

65d4fd2

- update roxygen - devtools::document() - rebuild pkg

egouldo added a commit that referenced this issue Jun 14, 2024

close #40 correctly subset collinearity effects_analysis

49c1431

- rerun tar_make() - rebuild package with updated data

egouldo mentioned this issue Jun 14, 2024

Investigate and fix rating analysis weights #42

Merged

egouldo closed this as completed in #42 Jun 14, 2024

egouldo reopened this Jul 29, 2024

egouldo added a commit that referenced this issue Jul 29, 2024

#40 need to group_by collinearity subset so blue tit `effects_analysi…

dc13254

…s` data is lumped together.

egouldo pinned this issue Jul 29, 2024

egouldo mentioned this issue Jul 29, 2024

Functionalise fit_MA_mv #29 #62

Merged

egouldo linked a pull request Sep 5, 2024 that will close this issue

fix bugs in package data regeneration for ManyAnalysts manuscript #148

Merged

egouldo removed a link to a pull request Sep 5, 2024

fix bugs in package data regeneration for ManyAnalysts manuscript #148

Merged

egouldo closed this as completed Sep 10, 2024

egouldo unpinned this issue Oct 9, 2024

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Collinearity subset analysis does not subset correct list-column of df's #40

Collinearity subset analysis does not subset correct list-column of df's #40

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Collinearity subset analysis does not subset correct list-column of df's #40

Collinearity subset analysis does not subset correct list-column of df's #40

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