Read data
Here we read the msa_models.rds file which contains the
estimates and standard errors obtained from refitting the analyses
submitted by the teams.
msa_models <- readRDS("./data/analyses/msa_models.rds") %>%
mutate(
outcome = as.factor(outcome),
typicality = as.factor(typicality),
temporal_window = as.factor(temporal_window)
) %>%
droplevels() %>%
mutate(
outcome = contr_code_sum(outcome),
typicality = contr_code_sum(typicality),
temporal_window = contr_code_sum(temporal_window)
)
priors_prereg <- c(
prior(normal(0, 1), class = Intercept),
prior(cauchy(0, 1), class = sd)
)
meta_bm_prereg <- brm(
estimate | se(se) ~ (1 | animal),
data = msa_models,
prior = priors_prereg,
chain = 4,
seed = my_seed,
cores = 4,
backend = "cmdstanr",
threads = threading(2),
file = "./data/meta_analysis/meta_bm_prereg.rds"
)
meta_bm_prereg
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: estimate | se(se) ~ (1 | animal)
## Data: msa_models (Number of observations: 192)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~animal (Number of levels: 33)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.04 0.01 0.03 0.07 1.00 1161 1902
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.00 0.01 -0.03 0.02 1.00 1386 1734
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.00 0.00 0.00 0.00 NA NA NA
##
## Draws were sampled using sample(hmc). 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).
Analytic and researcher-related predictors model
Weakly-informative regularising priors.
preds_priors_prereg <- c(
prior(normal(0, 1), class = Intercept),
prior(normal(0, 1), class = b),
prior(normal(0, 1), class = meanme),
prior(cauchy(0, 1), class = sdme),
prior(lkj(2), class = corme)
)
# contrast code
msa_models <- msa_models %>%
mutate(
# Measurement error cannot be 0 (model doesn't run), so adding a small number to it.
all_sd = ifelse(is.na(all_sd), 0.0001, all_sd),
years_from_phd_sd = ifelse(is.na(years_from_phd_sd) | years_from_phd_sd == 0, 0.0001, years_from_phd_sd),
prior_belief_sd = ifelse(is.na(prior_belief_sd) | prior_belief_sd == 0, 0.0001, prior_belief_sd)
)
predictors_bm_rintercepts_prereg <- brm(
estimate | se(se) ~
pop_sdi +
n_random +
n_models +
outcome + # major dimension
temporal_window + # temporal window
typicality +
me(all_rating, all_sd) +
me(years_from_phd, years_from_phd_sd) +
me(prior_belief, prior_belief_sd),
data = msa_models,
prior = preds_priors_prereg,
chain = 4,
iter = 4000,
control = list(adapt_delta = 0.9, max_treedepth = 13),
seed = my_seed,
cores = 4,
threads = threading(2),
backend = "cmdstanr",
file = "./data/meta_analysis/predictors_bm_rintercepts_prereg.rds"
)
predictors_bm_rintercepts_prereg
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be
## careful when analysing the results! We recommend running more iterations and/or
## setting stronger priors.
## Warning: There were 51 divergent transitions after warmup. Increasing
## adapt_delta above may help. See http://mc-stan.org/misc/warnings.html#divergent-
## transitions-after-warmup
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: estimate | se(se) ~ pop_sdi + n_random + n_models + outcome + temporal_window + typicality + me(all_rating, all_sd) + me(years_from_phd, years_from_phd_sd) + me(prior_belief, prior_belief_sd)
## Data: msa_models (Number of observations: 189)
## Draws: 4 chains, each with iter = 4000; warmup = 2000; thin = 1;
## total post-warmup draws = 8000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept 3.05 4.04 -0.74 9.73 4.08
## pop_sdi -0.21 0.81 -1.41 0.85 5.05
## n_random 0.43 0.71 -0.44 1.44 8.02
## n_models -0.84 0.59 -1.45 0.00 3.92
## outcome.durationMintercept 0.30 1.10 -1.22 1.85 8.71
## outcome.f0Mintercept -0.02 0.95 -1.47 1.15 10.94
## outcome.formantsMintercept 0.29 0.89 -0.62 1.76 4.55
## outcome.intensityMintercept -0.05 0.23 -0.39 0.26 5.46
## temporal_window.segmentMintercept 0.50 1.22 -1.25 1.72 9.45
## temporal_window.wordMintercept -0.11 1.02 -1.07 1.52 6.26
## temporal_window.phraseMintercept 0.13 1.33 -1.84 1.88 7.58
## temporal_window.sentenceMintercept -1.11 0.71 -2.00 -0.03 5.58
## typicality.categoricalMintercept -0.01 1.08 -1.43 1.61 4.93
## meall_ratingall_sd 0.36 1.00 -1.07 1.53 5.61
## meyears_from_phdyears_from_phd_sd 0.03 1.31 -1.18 1.96 6.08
## meprior_beliefprior_belief_sd 0.29 0.30 -0.04 0.66 3.96
## Bulk_ESS Tail_ESS
## Intercept 4 NA
## pop_sdi 4 4
## n_random 4 NA
## n_models 4 4
## outcome.durationMintercept 4 NA
## outcome.f0Mintercept 4 NA
## outcome.formantsMintercept 4 NA
## outcome.intensityMintercept 4 5
## temporal_window.segmentMintercept 4 NA
## temporal_window.wordMintercept 4 NA
## temporal_window.phraseMintercept 4 NA
## temporal_window.sentenceMintercept 4 4
## typicality.categoricalMintercept 4 4
## meall_ratingall_sd 4 NA
## meyears_from_phdyears_from_phd_sd 4 NA
## meprior_beliefprior_belief_sd 4 11
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.00 0.00 0.00 0.00 NA NA NA
##
## Draws were sampled using sample(hmc). 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).