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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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`filter_args` argument for `meta_analyse_datasets()` not behaving correctly in `yi` pipeline #132

Open egouldo opened 2 weeks ago

egouldo commented 2 weeks ago

Full reprex to be attached momentarily (comment too large for pasting), bug found in course of identifying problem in #130

Note for #130 will not fix, however, logging here for addressing software manuscript submission milestone.

egouldo commented 2 weeks ago

gaudy-geese_reprex.md

egouldo commented 2 weeks ago

Load Data

options(tidyverse.quiet = TRUE)
options(lifecycle_verbosity = "warning")
library(ManyEcoEvo)
#> Loading required package: rmarkdown
#> Loading required package: bookdown
#> Registered S3 method overwritten by 'parsnip':
#>   method          from 
#>   print.nullmodel vegan
#> Registered S3 method overwritten by 'lava':
#>   method         from    
#>   print.estimate EnvStats
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)

data("ManyEcoEvo_yi")

Compare Results with and without Exclusion Subsetting

with exclusion subsetting

yi_results_outlier_subsetting <-
  ManyEcoEvo_yi %>%
  prepare_response_variables(
    estimate_type = "yi",
    param_table =
      ManyEcoEvo:::analysis_data_param_tables,
    dataset_standardise = "blue tit",
    dataset_log_transform = "eucalyptus"
  ) %>%
  generate_yi_subsets() %>% # TODO: must be run after prepare_response_variables??
  apply_VZ_exclusions(
    VZ_colname = list(
      "eucalyptus" = "se_log",
      "blue tit" = "VZ"
    ),
    VZ_cutoff = 3
  ) %>%
  generate_exclusion_subsets() %>%
  generate_outlier_subsets(
    outcome_variable =
      list(
        dataset =
          list(
            "eucalyptus" = "mean_log",
            "blue tit" = "Z"
          )
      ),
    n_min = -3,
    n_max = -3,
    ignore_subsets = NULL
  ) %>%
  compute_MA_inputs()
#> 
#> ── Computing Sorensen diversity indices inputs ─────────────────────────────────
#> 
#> ── Applying exclusion rules and generating exclusion subsets ───────────────────
#> 
#> ── Generating out-of-sample prediction subsets. ────────────────────────────────
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#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ℹ Standardising and/or log-transforming response variables for "yi" estimates.
#> 
#> ── Computing meta-analysis inputsfor `estimate_type` = "yi" ────────────────────
#> 
#> ── Standardising out-of-sample predictions ──
#> 
#> ── Computing meta-analysis inputs: ─────────────────────────────────────────────
#> 
#> ── Log-transforming response-variable ──
#> 
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> 
#> ── Applying VZ exclusions ──────────────────────────────────────────────────────
#> ! `VZ_cutoff` = 3 was recycled to match the number of unique datasets in `df`.
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 2 extreme values of `VZ` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `VZ` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 2 extreme values of `VZ` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `se_log` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `se_log` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `se_log` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> ! `n_min` = -3 was recycled to match the number of unique datasets in `data`.
#> ! `n_max` = -3 was recycled to match the number of unique datasets in `data`.
#> Warning: Unquoting language objects with `!!!` is deprecated as of rlang 0.4.0. Please
#> use `!!` instead.
#> 
#> # Bad: dplyr::select(data, !!!enquo(x))
#> 
#> # Good: dplyr::select(data, !!enquo(x)) # Unquote single quosure
#> dplyr::select(data, !!!enquos(x)) # Splice list of quosures

without exclusion subsetting

yi_results_no_exclusion_subsetting_outlier_subsetting <-
  ManyEcoEvo_yi %>%
  prepare_response_variables(
    estimate_type = "yi",
    param_table =
      ManyEcoEvo:::analysis_data_param_tables,
    dataset_standardise = "blue tit",
    dataset_log_transform = "eucalyptus"
  ) %>%
  generate_yi_subsets() %>% # TODO: must be run after prepare_response_variables??
  apply_VZ_exclusions(
    VZ_colname = list(
      "eucalyptus" = "se_log",
      "blue tit" = "VZ"
    ),
    VZ_cutoff = 3
  ) %>%
  generate_outlier_subsets(
    outcome_variable =
      list(
        dataset =
          list(
            "eucalyptus" = "mean_log",
            "blue tit" = "Z"
          )
      ),
    n_min = -3,
    n_max = -3,
    ignore_subsets = NULL
  ) %>%
  compute_MA_inputs()
#> 
#> ── Computing Sorensen diversity indices inputs ─────────────────────────────────
#> 
#> ── Generating out-of-sample prediction subsets. ────────────────────────────────
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ✔ Applied back-transformation for squared effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for squared effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for squared effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for squared effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for squared effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for squared effect sizes or out-of-sample predictions.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ✔ Applied back-transformation for ^100 effect sizes or out of sample predictions.
#> ✔ Applied back-transformation for cubed effect sizes
#> ✔ Applied back-transformation for ^100 effect sizes or out of sample predictions.
#> ✔ Applied back-transformation for cubed effect sizes
#> ✔ Applied back-transformation for ^100 effect sizes or out of sample predictions.
#> ✔ Applied back-transformation for cubed effect sizes
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ✔ Applied back-transformation for ^14 effect sizes or out of sample predictions.
#> ✔ Applied back-transformation for ^14 effect sizes or out of sample predictions.
#> ✔ Applied back-transformation for ^14 effect sizes or out of sample predictions.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ℹ No back-transformation required, identity link used.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for logit-transformed effect sizes or out-of-sample predictions
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for square-root transformed effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for square-root transformed effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for square-root transformed effect sizes or out-of-sample predictions.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ✔ Applied back-transformation for log-transformed effect sizes or out-of-sample predictions, using 10000 simulations.
#> ℹ Standardising and/or log-transforming response variables for "yi" estimates.
#> 
#> ── Computing meta-analysis inputsfor `estimate_type` = "yi" ────────────────────
#> 
#> ── Standardising out-of-sample predictions ──
#> 
#> ── Computing meta-analysis inputs: ─────────────────────────────────────────────
#> 
#> ── Log-transforming response-variable ──
#> 
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> ✔ Log-transformed out-of-sample predictions, using 10000 simulations.
#> 
#> ── Applying VZ exclusions ──────────────────────────────────────────────────────
#> ! `VZ_cutoff` = 3 was recycled to match the number of unique datasets in `df`.
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 2 extreme values of `VZ` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `VZ` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 2 extreme values of `VZ` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `se_log` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `se_log` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> 
#> ── Excluding extreme values of VZ ──
#> 
#> → 0 extreme values of `se_log` removed at threshold of 3 for `dataset` "blue tit", `estimate_type` = "y25".
#> ! `n_min` = -3 was recycled to match the number of unique datasets in `data`.
#> ! `n_max` = -3 was recycled to match the number of unique datasets in `data`.
#> Warning: Unquoting language objects with `!!!` is deprecated as of rlang 0.4.0. Please
#> use `!!` instead.
#> 
#> # Bad: dplyr::select(data, !!!enquo(x))
#> 
#> # Good: dplyr::select(data, !!enquo(x)) # Unquote single quosure
#> dplyr::select(data, !!!enquos(x)) # Splice list of quosures

Check results

list(
  yi_results_outlier_subsetting,
  yi_results_no_exclusion_subsetting_outlier_subsetting
) %>%
  purrr::map(~ dplyr::group_by(.x, dplyr::pick(dplyr::any_of(c(
    "dataset",
    "estimate_type",
    "exclusion_set"
  )))) %>%
    dplyr::count())
#> [[1]]
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 1
#>  2 blue tit   y25           complete-rm_outliers     1
#>  3 blue tit   y50           complete                 1
#>  4 blue tit   y50           complete-rm_outliers     1
#>  5 blue tit   y75           complete                 1
#>  6 blue tit   y75           complete-rm_outliers     1
#>  7 eucalyptus y25           complete                 1
#>  8 eucalyptus y25           complete-rm_outliers     1
#>  9 eucalyptus y50           complete                 1
#> 10 eucalyptus y50           complete-rm_outliers     1
#> 11 eucalyptus y75           complete                 1
#> 12 eucalyptus y75           complete-rm_outliers     1
#> 
#> [[2]]
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 1
#>  2 blue tit   y25           complete-rm_outliers     1
#>  3 blue tit   y50           complete                 1
#>  4 blue tit   y50           complete-rm_outliers     1
#>  5 blue tit   y75           complete                 1
#>  6 blue tit   y75           complete-rm_outliers     1
#>  7 eucalyptus y25           complete                 1
#>  8 eucalyptus y25           complete-rm_outliers     1
#>  9 eucalyptus y50           complete                 1
#> 10 eucalyptus y50           complete-rm_outliers     1
#> 11 eucalyptus y75           complete                 1
#> 12 eucalyptus y75           complete-rm_outliers     1

Check Complete Targets Pipeline

pipeline_results_comparison <-
  tidyr::expand_grid(
    data =
      list(
        yi_results_outlier_subsetting,
        yi_results_no_exclusion_subsetting_outlier_subsetting
      ),
    filter_vars =
      list(
        NULL,
        rlang::expr(exclusion_set == "complete"),
        rlang::expr(exclusion_set != "complete")
      )
  ) %>%
  purrr::pmap(~ ..1 %>%
    meta_analyse_datasets(
      outcome_variable =
        list(
          dataset =
            list("eucalyptus" = "mean_log", "blue tit" = "Z")
        ),
      outcome_SE =
        list(
          dataset =
            list("eucalyptus" = "se_log", "blue tit" = "VZ")
        ),
      filter_vars = if (!is.null(..2)) list(..2)
    )) %>%
  purrr::set_names({
    tidyr::expand_grid(
      dataset = c(
        "outlier_subsetting",
        "no_exclusion_subsetting_outlier_subsetting"
      ),
      filter_args = c(
        "NULL filter_vars",
        "exclusion_set == 'complete'",
        "exclusion_set != 'complete"
      )
    ) %>%
      tidyr::unite("run_name", dataset, filter_args, sep = " x ") %>%
      purrr::flatten_chr()
  })
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.6639
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.3381
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.671
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.5984
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 10.7961
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 13.1214
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.7193
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.4206
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.6745
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.847
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 5.0402
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 9.7949
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> 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 0.34 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.14 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.22 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 3.32 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.53 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.51 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.88 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 10 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 4.34 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> Warning: There were 12 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> Caused by warning in `checkConv()`:
#> ! Model is nearly unidentifiable: very large eigenvalue
#>  - Rescale variables?
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 11 remaining warnings.
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.6639
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.3381
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.671
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.5984
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 10.7961
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 13.1214
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.7193
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.4206
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.6745
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.847
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 5.0402
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 9.7949
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.34 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.14 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.22 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 3.32 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.53 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.51 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.88 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 10 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 4.34 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> Warning: There were 12 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> Caused by warning in `checkConv()`:
#> ! Model is nearly unidentifiable: very large eigenvalue
#>  - Rescale variables?
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 11 remaining warnings.
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.6639
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.3381
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.671
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.5984
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 10.7961
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 13.1214
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.7193
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.4206
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.6745
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.847
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 5.0402
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 9.7949
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.34 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.14 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.22 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 3.32 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.53 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.51 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.88 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 10 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 4.34 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> Warning: There were 12 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> Caused by warning in `checkConv()`:
#> ! Model is nearly unidentifiable: very large eigenvalue
#>  - Rescale variables?
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 11 remaining warnings.
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.6618
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.3375
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.672
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.7128
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 12.2935
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 13.2023
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.7169
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.4209
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.6755
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 5.0849
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.8149
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 9.4491
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.33 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.07 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.21 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 4.16 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.58 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.55 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.36 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 5.15 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 5.34 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> Warning: There were 24 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> Caused by warning in `checkConv()`:
#> ! Model failed to converge with max|grad| = 2.48904 (tol = 0.002, component 1)
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 23 remaining warnings.
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> boundary (singular) fit: see help('isSingular')
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.6618
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.3375
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.672
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.7128
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 12.2935
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 13.2023
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.7169
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.4209
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.6755
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 5.0849
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.8149
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 9.4491
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.33 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.07 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.21 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 4.16 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.58 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.55 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.36 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 5.15 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 5.34 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> Warning: There were 24 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> Caused by warning in `checkConv()`:
#> ! Model failed to converge with max|grad| = 2.48904 (tol = 0.002, component 1)
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 23 remaining warnings.
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete
#> boundary (singular) fit: see help('isSingular')
#> 
#> ── Meta-analysing Datasets ─────────────────────────────────────────────────────
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Fitting metaregression ──
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.6618
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.3375
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.672
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.7128
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 12.2935
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 13.2023
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.7169
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.4209
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 0.6755
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 5.0849
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 4.8149
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = 9.4491
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.33 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.07 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.21 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 4.16 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 0 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.58 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.6 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "blue tit". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of blue tit dataset variables is 0.55 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 1.36 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 5.15 for `abs_deviation_score_estimate`.
#> 
#> ── Box-cox transforming absolute deviation scores for `dataset` = "eucalyptus". ──
#> 
#> ℹ Optimised Lambda used in Box-Cox Transformation of eucalyptus dataset variables is 5.34 for `abs_deviation_score_estimate`.
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting glm for box-cox transformed outcome with sorensen diversity index as predictor ──
#> 
#> ℹ
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with continuous ratings predictor `RateAnalysis` on Box-Cox transformed `outcome`: `box_cox_abs_deviation_score_estimate` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` on Box-Cox transformed `outcome`:  `box_cox_abs_deviation_score_estimate` ──
#> 
#> boundary (singular) fit: see help('isSingular')
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> 
#> ── Fitting glm for box-cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> 
#> ! Columns `mixed_model` and box_cox_abs_deviation_score_estimate missing. Returning NA.
#> Warning: There were 24 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = purrr::map(...)`.
#> Caused by warning in `checkConv()`:
#> ! Model failed to converge with max|grad| = 2.48904 (tol = 0.002, component 1)
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 23 remaining warnings.
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')
#> ℹ Presence of random effects in analyses excluded as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete-rm_outliers
#> boundary (singular) fit: see help('isSingular')

pipeline_results_comparison %>%
  purrr::map(~ dplyr::group_by(.x, dplyr::pick(dplyr::any_of(c(
    "dataset",
    "estimate_type",
    "exclusion_set"
  )))) %>%
    dplyr::count())
#> $`outlier_subsetting x NULL filter_vars`
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 1
#>  2 blue tit   y25           complete-rm_outliers     1
#>  3 blue tit   y50           complete                 1
#>  4 blue tit   y50           complete-rm_outliers     1
#>  5 blue tit   y75           complete                 1
#>  6 blue tit   y75           complete-rm_outliers     1
#>  7 eucalyptus y25           complete                 1
#>  8 eucalyptus y25           complete-rm_outliers     1
#>  9 eucalyptus y50           complete                 1
#> 10 eucalyptus y50           complete-rm_outliers     1
#> 11 eucalyptus y75           complete                 1
#> 12 eucalyptus y75           complete-rm_outliers     1
#> 
#> $`outlier_subsetting x exclusion_set == 'complete'`
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 3
#>  2 blue tit   y25           complete-rm_outliers     1
#>  3 blue tit   y50           complete                 3
#>  4 blue tit   y50           complete-rm_outliers     1
#>  5 blue tit   y75           complete                 3
#>  6 blue tit   y75           complete-rm_outliers     1
#>  7 eucalyptus y25           complete                 3
#>  8 eucalyptus y25           complete-rm_outliers     1
#>  9 eucalyptus y50           complete                 3
#> 10 eucalyptus y50           complete-rm_outliers     1
#> 11 eucalyptus y75           complete                 3
#> 12 eucalyptus y75           complete-rm_outliers     1
#> 
#> $`outlier_subsetting x exclusion_set != 'complete`
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 1
#>  2 blue tit   y25           complete-rm_outliers     3
#>  3 blue tit   y50           complete                 1
#>  4 blue tit   y50           complete-rm_outliers     3
#>  5 blue tit   y75           complete                 1
#>  6 blue tit   y75           complete-rm_outliers     3
#>  7 eucalyptus y25           complete                 1
#>  8 eucalyptus y25           complete-rm_outliers     3
#>  9 eucalyptus y50           complete                 1
#> 10 eucalyptus y50           complete-rm_outliers     3
#> 11 eucalyptus y75           complete                 1
#> 12 eucalyptus y75           complete-rm_outliers     3
#> 
#> $`no_exclusion_subsetting_outlier_subsetting x NULL filter_vars`
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 1
#>  2 blue tit   y25           complete-rm_outliers     1
#>  3 blue tit   y50           complete                 1
#>  4 blue tit   y50           complete-rm_outliers     1
#>  5 blue tit   y75           complete                 1
#>  6 blue tit   y75           complete-rm_outliers     1
#>  7 eucalyptus y25           complete                 1
#>  8 eucalyptus y25           complete-rm_outliers     1
#>  9 eucalyptus y50           complete                 1
#> 10 eucalyptus y50           complete-rm_outliers     1
#> 11 eucalyptus y75           complete                 1
#> 12 eucalyptus y75           complete-rm_outliers     1
#> 
#> $`no_exclusion_subsetting_outlier_subsetting x exclusion_set == 'complete'`
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 3
#>  2 blue tit   y25           complete-rm_outliers     1
#>  3 blue tit   y50           complete                 3
#>  4 blue tit   y50           complete-rm_outliers     1
#>  5 blue tit   y75           complete                 3
#>  6 blue tit   y75           complete-rm_outliers     1
#>  7 eucalyptus y25           complete                 3
#>  8 eucalyptus y25           complete-rm_outliers     1
#>  9 eucalyptus y50           complete                 3
#> 10 eucalyptus y50           complete-rm_outliers     1
#> 11 eucalyptus y75           complete                 3
#> 12 eucalyptus y75           complete-rm_outliers     1
#> 
#> $`no_exclusion_subsetting_outlier_subsetting x exclusion_set != 'complete`
#> # A tibble: 12 × 4
#> # Groups:   dataset, estimate_type, exclusion_set [12]
#>    dataset    estimate_type exclusion_set            n
#>    <chr>      <chr>         <chr>                <int>
#>  1 blue tit   y25           complete                 1
#>  2 blue tit   y25           complete-rm_outliers     3
#>  3 blue tit   y50           complete                 1
#>  4 blue tit   y50           complete-rm_outliers     3
#>  5 blue tit   y75           complete                 1
#>  6 blue tit   y75           complete-rm_outliers     3
#>  7 eucalyptus y25           complete                 1
#>  8 eucalyptus y25           complete-rm_outliers     3
#>  9 eucalyptus y50           complete                 1
#> 10 eucalyptus y50           complete-rm_outliers     3
#> 11 eucalyptus y75           complete                 1
#> 12 eucalyptus y75           complete-rm_outliers     3

Created on 2024-08-29 with reprex v2.1.0