Analysis of Five Year Impact Factor Effect

Adam H. Sparks

2023-01-04

Source: vignettes/h_analysis_5y_if.Rmd

This vignette documents the analysis of the data gathered from surveying 21 journals and 450 articles in the field of plant pathology for their openness and reproducibility and the effect that the journal’s 5-year impact factor had on that score.

Set-up Workspace

Load libraries used and setting the ggplot2 theme for the document.

Priors

Priors were set to be generic weakly informative.

priors <- c(prior(normal(0, 1), class = "b"),
            prior(normal(0, 1), class = "Intercept"))

Five Year Impact Factor Model

Computational Methods Availability

Test the effect that journal’s five year impact factor had on the availability of code.

rrpp <- import_notes()
rrpp <- drop_na(rrpp, comp_mthds_avail)

m_h1 <-
  brm(
    formula = comp_mthds_avail ~ IF_5year +
      (1 | assignee),
    data = rrpp,
    seed = 27,
    prior = priors,
    family = cumulative(link = "logit"),
    control = list(adapt_delta = 0.99),
    iter = 10000
  )
#> Compiling Stan program...
#> Start sampling

summary(m_h1)
#>  Family: cumulative 
#>   Links: mu = logit; disc = identity 
#> Formula: comp_mthds_avail ~ IF_5year + (1 | assignee) 
#>    Data: rrpp (Number of observations: 440) 
#>   Draws: 4 chains, each with iter = 10000; warmup = 5000; thin = 1;
#>          total post-warmup draws = 20000
#> 
#> Group-Level Effects: 
#> ~assignee (Number of levels: 5) 
#>               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept)     6.07      2.60     2.71    12.71 1.00     5374     9397
#> 
#> Population-Level Effects: 
#>              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept[1]     1.53      1.02    -0.45     3.52 1.00    13716    12172
#> Intercept[2]     1.92      1.02    -0.03     3.93 1.00    14698    12608
#> IF_5year         0.46      0.27    -0.05     1.02 1.00    13942    11307
#> 
#> Family Specific Parameters: 
#>      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> disc     1.00      0.00     1.00     1.00   NA       NA       NA
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

plot(m_h1)


pp_check(m_h1, type = "bars", draws = 50)
#> Using 10 posterior draws for ppc type 'bars' by default.
#> Warning: The following arguments were unrecognized and ignored: draws


plot(equivalence_test(m_h1))
#> Picking joint bandwidth of 0.0834
#> Warning: Removed 3000 rows containing non-finite values
#> (`stat_density_ridges()`).


pander(m_h1_report <- report(m_h1))
#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.
#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.
#> Start sampling
#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.

#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.

_We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._ and _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict comp_mthds_avail with IF_5year (formula: comp_mthds_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as student_t (location = 0.00, scale = 2.50) distributions. The model’s explanatory power is weak (R2 = 0.02, 95% CI [1.35e-04, 0.11]) and the part related to the fixed effects alone (marginal R2) is of 0.42 (95% CI [0.05, 0.49]). Within this model:

  • The effect of b Intercept[1] (Median = 1.53, 95% CI [-0.45, 3.52]) has a 93.53% probability of being positive (> 0), 92.92% of being significant (> 0.05), and 88.72% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13651)
  • The effect of b Intercept[2] (Median = 1.92, 95% CI [-0.03, 3.93]) has a 97.28% probability of being positive (> 0), 96.85% of being significant (> 0.05), and 94.70% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 13661)
  • The effect of b IF 5year (Median = 0.46, 95% CI [-0.05, 1.02]) has a 96.20% probability of being positive (> 0), 94.27% of being significant (> 0.05), and 72.47% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14627)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._


m_h1_es <- report_effectsize(m_h1)
#> Start sampling

Data Availability

Test for any effects of the five year impact factor on the data’s availability.

rrpp <- import_notes()
rrpp <- drop_na(rrpp, data_avail)

m_h2 <-
  brm(
    formula = data_avail ~ IF_5year +
      (1 | assignee),
    data = rrpp,
    seed = 27,
    prior = priors,
    family = cumulative(link = "logit"),
    control = list(adapt_delta = 0.99),
    iter = 10000,
    chains = 4
  )
#> Compiling Stan program...
#> Start sampling

summary(m_h2)
#>  Family: cumulative 
#>   Links: mu = logit; disc = identity 
#> Formula: data_avail ~ IF_5year + (1 | assignee) 
#>    Data: rrpp (Number of observations: 448) 
#>   Draws: 4 chains, each with iter = 10000; warmup = 5000; thin = 1;
#>          total post-warmup draws = 20000
#> 
#> Group-Level Effects: 
#> ~assignee (Number of levels: 5) 
#>               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept)     2.16      1.30     0.33     5.37 1.00     2873     3374
#> 
#> Population-Level Effects: 
#>              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept[1]     0.84      0.67    -0.52     2.04 1.00     4267     7337
#> Intercept[2]     1.09      0.67    -0.26     2.30 1.00     4304     7608
#> Intercept[3]     1.53      0.67     0.18     2.73 1.00     4335     6953
#> IF_5year         0.15      0.08    -0.01     0.30 1.00    14647    12751
#> 
#> Family Specific Parameters: 
#>      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> disc     1.00      0.00     1.00     1.00   NA       NA       NA
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

plot(m_h2)


pp_check(m_h2, type = "bars", draws = 50)
#> Using 10 posterior draws for ppc type 'bars' by default.
#> Warning: The following arguments were unrecognized and ignored: draws


plot(equivalence_test(m_h2))
#> Picking joint bandwidth of 0.058
#> Warning: Removed 4000 rows containing non-finite values
#> (`stat_density_ridges()`).


pander(m_h2_report <- report(m_h2))
#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.
#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.
#> Start sampling
#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.

#> Warning: Predictions are treated as continuous variables in 'bayes_R2' which is
#> likely invalid for ordinal families.

_We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._, _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as normal (mean = 0.00, SD = 1.00) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._ and _We fitted a Bayesian logistic mixed model (estimated using MCMC sampling with 4 chains of 10000 iterations and a warmup of 5000) to predict data_avail with IF_5year (formula: data_avail ~ IF_5year). The model included assignee as random effect (formula: ~1 | assignee). Priors over parameters were set as student_t (location = 0.00, scale = 2.50) distributions. The model’s explanatory power is weak (R2 = 0.03, 95% CI [7.96e-03, 0.06]) and the part related to the fixed effects alone (marginal R2) is of 0.02 (95% CI [3.91e-10, 0.07]). Within this model:

  • The effect of b Intercept[1] (Median = 0.85, 95% CI [-0.52, 2.04]) has a 89.17% probability of being positive (> 0), 87.71% of being significant (> 0.05), and 78.25% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 14614)
  • The effect of b Intercept[2] (Median = 1.11, 95% CI [-0.26, 2.30]) has a 94.50% probability of being positive (> 0), 93.73% of being significant (> 0.05), and 87.67% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4325)
  • The effect of b Intercept[3] (Median = 1.54, 95% CI [0.18, 2.73]) has a 98.71% probability of being positive (> 0), 98.48% of being significant (> 0.05), and 96.35% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4363)
  • The effect of b IF 5year (Median = 0.15, 95% CI [-6.48e-03, 0.30]) has a 96.95% probability of being positive (> 0), 89.42% of being significant (> 0.05), and 2.37% of being large (> 0.30). The estimation successfully converged (Rhat = 1.000) and the indices are reliable (ESS = 4390)

Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|. Convergence and stability of the Bayesian sampling has been assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017)._


m_h2_es <- report_effectsize(m_h2)
#> Start sampling

Save Model Objects

Save the model objects for figures in the paper.

save(m_h1, file = here("inst/extdata/m_h1.Rda"))
save(m_h2, file = here("inst/extdata/m_h2.Rda"))

save(m_h1_report, file = here("inst/extdata/m_h1_report.Rda"))
save(m_h2_report, file = here("inst/extdata/m_h2_report.Rda"))

save(m_h1_es, file = here("inst/extdata/m_h1_es.Rda"))
save(m_h2_es, file = here("inst/extdata/m_h2_es.Rda"))

Colophon 

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.2 (2022-10-31)
#>  os       macOS Ventura 13.1
#>  system   aarch64, darwin20
#>  ui       X11
#>  language en
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Australia/Perth
#>  date     2023-01-04
#>  pandoc   2.19.2 @ /opt/homebrew/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
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