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egouldo / ManyEcoEvo

Software for analysing Many-Analysts' style data and generating the ManyEcoEvo project data
https://egouldo.github.io/ManyEcoEvo/
GNU General Public License v3.0
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Collinearity subset analysis does not subset correct list-column of df's #40

Open egouldo opened 2 months ago

egouldo commented 2 months ago

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

https://github.com/egouldo/ManyEcoEvo/blob/77c89f6bb61815e772b3e28e33411c3f522b5422/R/generate_collinearity_subset.R#L53-L55

egouldo commented 2 months ago

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 commented 2 months ago

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 commented 2 months ago

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)`
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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 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

egouldo commented 2 months ago

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

egouldo commented 1 month ago

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 !

egouldo commented 4 weeks ago

Potentially related to #75 ??

egouldo commented 1 week ago

Double check after updates on #121