Bug fixes for Manuscript submission #130

egouldo · 2024-08-29T01:53:43Z

Bug fixes required for rendering Manuscript:

Function fixes

tidy_mod_summary column in make_viz() results should also include studies, i.e.:

broom::tidy(.x, conf.int = TRUE, include_studies = TRUE)

Targets Outputs

NA returned for ALL results of fit_uni_mixed_effects():

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
ManyEcoEvo_results$uni_mixed_effects %>% purrr::keep(~ is.na(.x))
#> [[1]]
#> [1] NA
#> 
#> [[2]]
#> [1] NA
#> 
#> [[3]]
#> [1] NA
#> 
#> [[4]]
#> [1] NA
#> 
#> [[5]]
#> [1] NA
#> 
#> [[6]]
#> [1] NA
#> 
#> [[7]]
#> [1] NA
#> 
#> [[8]]
#> [1] NA
#> 
#> [[9]]
#> [1] NA
#> 
#> [[10]]
#> [1] NA
#> 
#> [[11]]
#> [1] NA
#> 
#> [[12]]
#> [1] NA
#> 
#> [[13]]
#> [1] NA
#> 
#> [[14]]
#> [1] NA
#> 
#> [[15]]
#> [1] NA
#> 
#> [[16]]
#> [1] NA
#> 
#> [[17]]
#> [1] NA

^{Created on 2024-08-29 with reprex v2.1.0}

duplicate rows in ManyEcoEvo_yi_results object 🤔

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

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
data("ManyEcoEvo_yi_results")
ManyEcoEvo_yi_results %>%
  dplyr::group_by(dataset, estimate_type, exclusion_set) %>%
  dplyr::count()
#> # 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

^{Created on 2024-08-29 with reprex v2.1.0}

But Zr pipeline ok:

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

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
data("ManyEcoEvo_results")
ManyEcoEvo_results %>%
  dplyr::group_by(pick(ends_with("set"))) %>%
  dplyr::count() %>%
  knitr::kable()

exclusion_set	dataset	publishable_subset	expertise_subset	collinearity_subset	n
complete	blue tit	All	All	All	1
complete	blue tit	All	All	collinearity_removed	1
complete	blue tit	All	expert	All	1
complete	blue tit	data_flawed	All	All	1
complete	blue tit	data_flawed_major	All	All	1
complete	eucalyptus	All	All	All	1
complete	eucalyptus	All	expert	All	1
complete	eucalyptus	data_flawed	All	All	1
complete	eucalyptus	data_flawed_major	All	All	1
partial	blue tit	All	All	All	1
partial	blue tit	data_flawed	All	All	1
partial	blue tit	data_flawed_major	All	All	1
partial	eucalyptus	All	All	All	1
partial	eucalyptus	data_flawed	All	All	1
partial	eucalyptus	data_flawed_major	All	All	1
partial-rm_outliers	blue tit	All	All	All	1
partial-rm_outliers	eucalyptus	All	All	All	1

^{Created on 2024-08-29 with reprex v2.1.0}

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

egouldo · 2024-08-29T04:26:13Z

Found source of issue for duplicate analysis results in yi pipeline.

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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#> 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.
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#> 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.
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#> 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.
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#> 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.
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#> 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

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}

```r #> 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 ```

- Added conditional behaviour for when character vector supplied - feat!: added arg checks #116 and cli output for when this condition is triggered --- But wasn't failing for `yi` because `yi` received `rlang::expressions()` while `Zr` call used single length character variable for `outcome_variable` and `outcome_SE`

feat!: add threshold for executing function in line with preregistered threshold (5 in each category of `mixed_model`) style: move cli output after argument checking #97 docs: capitalise Box-Cox #102

--- failed to be triggered as result needed to evaluate to TRUE for required expression to evaluate

--- forgot to add `c()` around character strings for cli message

egouldo · 2024-08-29T11:30:04Z

Fixed bug in meta_analyse_datasets() wrapper:

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

devtools::load_all()
#> ℹ Loading 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

check_ManyEcoEvo_results <- ManyEcoEvo %>%
  prepare_response_variables(
    estimate_type = "Zr",
    dataset_standardise =
      c("blue tit", "eucalyptus")
  ) |>
  generate_exclusion_subsets(estimate_type = "Zr") |>
  generate_rating_subsets() |>
  generate_expertise_subsets(
    ManyEcoEvo:::expert_subset
  ) |>
  generate_collinearity_subset(
    ManyEcoEvo:::collinearity_subset
  ) |>
  generate_outlier_subsets(
    outcome_variable =
      list(dataset = list(
        "eucalyptus" = "Zr",
        "blue tit" = "Zr"
      )),
    n_min = -2,
    n_max = -2,
    ignore_subsets =
      rlang::exprs(
        collinearity_subset != "collinearity_removed",
        expertise_subset != "expert",
        publishable_subset == "All",
        exclusion_set != "complete"
      )
  ) |>
  compute_MA_inputs(estimate_type = "Zr") |>
  meta_analyse_datasets(
    outcome_variable = "Zr",
    outcome_SE = "VZr",
    filter_vars =
      rlang::exprs(
        exclusion_set == "complete",
        expertise_subset == "All",
        publishable_subset == "All",
        collinearity_subset == "All"
      )
  )
#> 
#> ── Computing Sorensen diversity indices inputs ─────────────────────────────────
#> 
#> ── Applying exclusion rules and generating exclusion subsets ───────────────────
#> ℹ Standardising response variables for "Zr" estimates.
#> 
#> ── Computing meta-analysis inputsfor `estimate_type` = "Zr" ────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 484.0193.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 666.56874.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 590.18263.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.006225,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.003996,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df 481.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.09247,
#> 3. adjusted_df 316.17526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.029,
#> 3. adjusted_df 366.3.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.042,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01416305,
#> 3. adjusted_df 257.905.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.030382264,
#> 3. adjusted_df 2372.82.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01409,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.008781,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.014853,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.000769,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.033978443,
#> 3. adjusted_df 347.4992526.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01823188,
#> 3. adjusted_df 55.44391.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02039768,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.02496,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.03575,
#> 3. adjusted_df NA.
#> 
#> ── Computing meta-analysis inputsfor `estimate_type` = "Zr" ────────────────────
#> 
#> ── Computing standardised effect sizes `Zr` and variance `VZr` ──
#> 
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.29212,
#> 3. adjusted_df 21.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007831,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.07216,
#> 3. adjusted_df 0.560867697.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.57,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.2328,
#> 3. adjusted_df 343.24787.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.3188723,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0059286,
#> 3. adjusted_df 1.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.007385,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.052462,
#> 3. adjusted_df 3.5e-25.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 8.98,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 7.97,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.19,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 18.5,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 5.92,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.529,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 2.89,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.605,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.01312667,
#> 3. adjusted_df -2.6269353.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.197,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.192,
#> 3. adjusted_df 82.703.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.1,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se NA,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.0048042,
#> 3. adjusted_df 341.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.21,
#> 3. adjusted_df NA.
#> ✖ Required values for computing standardised effect sizes missing:
#> ! Returning "NA" for tupple:
#> 1. beta_estimate NA,
#> 2. beta_se 0.5756016,
#> 3. adjusted_df 3.536992.
#> ! `n_min` = -2 was recycled to match the number of unique datasets in `data`.
#> ! `n_max` = -2 was recycled to match the number of unique datasets in `data`.
#> ! Column `estimate_type` already exists in `ManyEcoEvo`, and will be overwritten by supplied value of `estimate_type` = "Zr"
#> 
#> ── 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 ──
#> 
#> ── 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` = -0.3512
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.0922
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.3587
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.1074
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.363
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.3699
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.0247
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.0422
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.3743
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.3699
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.0274
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.0516
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.3606
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.1659
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.3584
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.3636
#> 
#> ── Calculating absolute deviation scores from meta-analytic mean ──
#> 
#> ℹ Using the meta-analytic mean outcome as the reference point:`meta_analytic_mean` = -0.038
#> 
#> ── 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 0.27 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 0.18 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 0.55 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 0.02 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 0.15 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 0.02 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 0.21 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 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 0.51 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 0.63 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`.
#> 
#> ── 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 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 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> 
#> ── Fitting `lmer()` with categorical ratings predictor `PublishableAsIs` 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` ──
#> # A tibble: 2 × 2
#>   mixed_model     n
#>         <dbl> <int>
#> 1           0     3
#> 2           1   128
#> 
#> ── Fitting glm for Box-Cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> # A tibble: 2 × 2
#>   mixed_model     n
#>         <dbl> <int>
#> 1           0     3
#> 2           1   115
#> 
#> ── Fitting glm for Box-Cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> # A tibble: 2 × 2
#>   mixed_model     n
#>         <dbl> <int>
#> 1           0     2
#> 2           1   107
#> 
#> ── Fitting glm for Box-Cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> # A tibble: 2 × 2
#>   mixed_model     n
#>         <dbl> <int>
#> 1           0     2
#> 2           1    98
#> 
#> ── Fitting glm for Box-Cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> # A tibble: 2 × 2
#>   mixed_model     n
#>         <dbl> <int>
#> 1           0     1
#> 2           1    88
#> 
#> ── Fitting glm for Box-Cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> # A tibble: 2 × 2
#>   mixed_model     n
#>         <dbl> <int>
#> 1           0     3
#> 2           1   114
#> # A tibble: 2 × 2
#>   mixed_model     n
#>         <dbl> <int>
#> 1           0     3
#> 2           1   111
#> 
#> ── Fitting glm for Box-Cox transformed outcome with inclusion of random effects (binary variable) as predictor ──
#> Warning: There were 45 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `box_cox_rating_cont = list(...)`.
#> ℹ In row 1.
#> Caused by warning in `optwrap()`:
#> ! convergence code -4 from nloptwrap: NLOPT_ROUNDOFF_LIMITED: Roundoff errors led to a breakdown of the optimization algorithm. In this case, the returned minimum may still be useful. (e.g. this error occurs in NEWUOA if one tries to achieve a tolerance too close to machine precision.)
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 44 remaining warnings.
#> ℹ Presence of random effects in analyses included as predictor in model for data subset:
#> • dataset: blue tit
#> • exclusion_set: complete
#> • expertise_subset: All
#> • publishable_subset: All
#> • collinearity_subset: All
#> ℹ Presence of random effects in analyses included as predictor in model for data subset:
#> • dataset: eucalyptus
#> • exclusion_set: complete
#> • expertise_subset: All
#> • publishable_subset: All
#> • collinearity_subset: All

check_ManyEcoEvo_results$uni_mixed_effects %>% map_lgl(rlang::is_na)
#>  [1]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#> [13]  TRUE FALSE  TRUE  TRUE FALSE

^{Created on 2024-08-29 with reprex v2.1.0}

data subset columns now NOT explicitly named, only model outputs from `meta_analyse_datasets()` and `prepare_response_variables()` --- fixes for #130 broke `make_viz()`

was originally only included to generate hard-coded columns, now no longer needed with #121 / #130 updates, and was never planned: <https://egouldo.github.io/ManyAnalysts/#out-of-sample-predictions-y_i-2>

`broom::tidy()` doesn't have method for `lmerMod` class

build!: rebuild targets pipeline, update version

egouldo added the bug an unexpected problem or unintended behavior label Aug 29, 2024

egouldo added this to the Respond Reviewer Comments milestone Aug 29, 2024

egouldo self-assigned this Aug 29, 2024

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

bug!: #130 include studies in tidy model summary outputs

9347d89

egouldo mentioned this issue Aug 29, 2024

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

Open

egouldo closed this as completed Aug 29, 2024

egouldo reopened this Aug 29, 2024

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

bug!: fix conditional logic check for testing threshold meet N #130

571f09f

--- failed to be triggered as result needed to evaluate to TRUE for required expression to evaluate

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

bug: fix error in fit_uni_mixed_effects() #130

1027140

--- forgot to add `c()` around character strings for cli message

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

bug!: ungroup() results of rowwise() #130

f145387

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

bug!: generalise make_viz()

32dc3f4

data subset columns now NOT explicitly named, only model outputs from `meta_analyse_datasets()` and `prepare_response_variables()` --- fixes for #130 broke `make_viz()`

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

bug!: revert back to using broom.mixed::tidy() #130

ec3ac1a

`broom::tidy()` doesn't have method for `lmerMod` class

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

close #130

3956c8b

build!: rebuild targets pipeline, update version

egouldo mentioned this issue Aug 29, 2024

Fix errors for manuscript #133

Merged

egouldo closed this as completed in #133 Aug 29, 2024

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Bug fixes for Manuscript submission #130

Bug fixes for Manuscript submission #130

egouldo commented Aug 29, 2024 •

edited

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egouldo commented Aug 29, 2024

egouldo commented Aug 29, 2024

Bug fixes for Manuscript submission #130

Bug fixes for Manuscript submission #130

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egouldo commented Aug 29, 2024 • edited Loading

Bug fixes required for rendering Manuscript:

Function fixes

Targets Outputs

egouldo commented Aug 29, 2024

Load Data

Compare Results with and without Exclusion Subsetting

with exclusion subsetting

without exclusion subsetting

Check results

Check Complete Targets Pipeline

egouldo commented Aug 29, 2024

egouldo commented Aug 29, 2024 •

edited

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