Prints summary of "RoBMA" ensemble implied by the specified priors
Source: R/check-input-and-settings.R
check_setup.Rdcheck_setup prints summary of "RoBMA" ensemble
implied by the specified prior distributions. It is useful for checking
the ensemble configuration prior to fitting all of the models.
Usage
check_setup(
model_type = NULL,
priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
scale = 0.15)),
priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
"one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12),
prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
"one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
0)),
priors_bias_null = prior_none(),
priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
priors_hierarchical_null = NULL,
models = FALSE,
silent = FALSE
)
check_setup.RoBMA(
model_type = NULL,
priors_effect = prior(distribution = "normal", parameters = list(mean = 0, sd = 1)),
priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1,
scale = 0.15)),
priors_bias = list(prior_weightfunction(distribution = "two.sided", parameters =
list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1,
1), steps = c(0.05, 0.1)), prior_weights = 1/12), prior_weightfunction(distribution =
"one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights =
1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha =
c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12),
prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1,
1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution =
"one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)),
prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0, 1),
truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy",
parameters = list(0, 5), truncation = list(0, Inf), prior_weights = 1/4)),
priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
0)),
priors_bias_null = prior_none(),
priors_hierarchical = prior("beta", parameters = list(alpha = 1, beta = 1)),
priors_hierarchical_null = NULL,
models = FALSE,
silent = FALSE
)Arguments
- model_type
string specifying the RoBMA ensemble. Defaults to
NULL. The other options are"PSMA","PP", and"2w"which override settings passed to thepriors_effect,priors_heterogeneity,priors_effect,priors_effect_null,priors_heterogeneity_null,priors_bias_null, andpriors_effect. See details for more information about the different model types.- priors_effect
list of prior distributions for the effect size (
mu) parameter that will be treated as belonging to the alternative hypothesis. Defaults to a standard normal distributionprior(distribution = "normal", parameters = list(mean = 0, sd = 1)).- priors_heterogeneity
list of prior distributions for the heterogeneity
tauparameter that will be treated as belonging to the alternative hypothesis. Defaults toprior(distribution = "invgamma", parameters = list(shape = 1, scale = .15))that is based on heterogeneities estimates from psychology (van Erp et al. 2017) .- priors_bias
list of prior distributions for the publication bias adjustment component that will be treated as belonging to the alternative hypothesis. Defaults to
list( prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) ), corresponding to the RoBMA-PSMA model introduce by Bartoš et al. (2023) .- priors_effect_null
list of prior distributions for the effect size (
mu) parameter that will be treated as belonging to the null hypothesis. Defaults to a point null hypotheses at zero,prior(distribution = "point", parameters = list(location = 0)).- priors_heterogeneity_null
list of prior distributions for the heterogeneity
tauparameter that will be treated as belonging to the null hypothesis. Defaults to a point null hypotheses at zero (a fixed effect meta-analytic models),prior(distribution = "point", parameters = list(location = 0)).- priors_bias_null
list of prior weight functions for the
omegaparameter that will be treated as belonging to the null hypothesis. Defaults no publication bias adjustment,prior_none().- priors_hierarchical
list of prior distributions for the correlation of random effects (
rho) parameter that will be treated as belonging to the alternative hypothesis. This setting allows users to fit a hierarchical (three-level) meta-analysis whenstudy_idsare supplied. Note that this is an experimental feature and see News for more details. Defaults to a beta distributionprior(distribution = "beta", parameters = list(alpha = 1, beta = 1)).- priors_hierarchical_null
list of prior distributions for the correlation of random effects (
rho) parameter that will be treated as belonging to the null hypothesis. Defaults toNULL.- models
should the models' details be printed.
- silent
do not print the results.
Examples
# check default RoBMA setup
check_setup()
#> Robust Bayesian meta-analysis (set-up)
#> Components summary:
#> Models Prior prob.
#> Effect 18/36 0.500
#> Heterogeneity 18/36 0.500
#> Bias 32/36 0.500
# check setup with custom priors
check_setup(
priors_effect = prior("normal", list(mean = 0, sd = 0.5)),
priors_heterogeneity = prior("invgamma", list(shape = 2, scale = 0.3)),
models = TRUE
)
#> Robust Bayesian meta-analysis (set-up)
#> Components summary:
#> Models Prior prob.
#> Effect 18/36 0.500
#> Heterogeneity 18/36 0.500
#> Bias 32/36 0.500
#>
#> Models overview:
#> Model Prior Effect Prior Heterogeneity
#> 1 Spike(0) Spike(0)
#> 2 Spike(0) Spike(0)
#> 3 Spike(0) Spike(0)
#> 4 Spike(0) Spike(0)
#> 5 Spike(0) Spike(0)
#> 6 Spike(0) Spike(0)
#> 7 Spike(0) Spike(0)
#> 8 Spike(0) Spike(0)
#> 9 Spike(0) Spike(0)
#> 10 Spike(0) InvGamma(2, 0.3)
#> 11 Spike(0) InvGamma(2, 0.3)
#> 12 Spike(0) InvGamma(2, 0.3)
#> 13 Spike(0) InvGamma(2, 0.3)
#> 14 Spike(0) InvGamma(2, 0.3)
#> 15 Spike(0) InvGamma(2, 0.3)
#> 16 Spike(0) InvGamma(2, 0.3)
#> 17 Spike(0) InvGamma(2, 0.3)
#> 18 Spike(0) InvGamma(2, 0.3)
#> 19 Normal(0, 0.5) Spike(0)
#> 20 Normal(0, 0.5) Spike(0)
#> 21 Normal(0, 0.5) Spike(0)
#> 22 Normal(0, 0.5) Spike(0)
#> 23 Normal(0, 0.5) Spike(0)
#> 24 Normal(0, 0.5) Spike(0)
#> 25 Normal(0, 0.5) Spike(0)
#> 26 Normal(0, 0.5) Spike(0)
#> 27 Normal(0, 0.5) Spike(0)
#> 28 Normal(0, 0.5) InvGamma(2, 0.3)
#> 29 Normal(0, 0.5) InvGamma(2, 0.3)
#> 30 Normal(0, 0.5) InvGamma(2, 0.3)
#> 31 Normal(0, 0.5) InvGamma(2, 0.3)
#> 32 Normal(0, 0.5) InvGamma(2, 0.3)
#> 33 Normal(0, 0.5) InvGamma(2, 0.3)
#> 34 Normal(0, 0.5) InvGamma(2, 0.3)
#> 35 Normal(0, 0.5) InvGamma(2, 0.3)
#> 36 Normal(0, 0.5) InvGamma(2, 0.3)
#> Prior Bias Prior prob.
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031
# show detailed model overview
check_setup(models = TRUE)
#> Robust Bayesian meta-analysis (set-up)
#> Components summary:
#> Models Prior prob.
#> Effect 18/36 0.500
#> Heterogeneity 18/36 0.500
#> Bias 32/36 0.500
#>
#> Models overview:
#> Model Prior Effect Prior Heterogeneity
#> 1 Spike(0) Spike(0)
#> 2 Spike(0) Spike(0)
#> 3 Spike(0) Spike(0)
#> 4 Spike(0) Spike(0)
#> 5 Spike(0) Spike(0)
#> 6 Spike(0) Spike(0)
#> 7 Spike(0) Spike(0)
#> 8 Spike(0) Spike(0)
#> 9 Spike(0) Spike(0)
#> 10 Spike(0) InvGamma(1, 0.15)
#> 11 Spike(0) InvGamma(1, 0.15)
#> 12 Spike(0) InvGamma(1, 0.15)
#> 13 Spike(0) InvGamma(1, 0.15)
#> 14 Spike(0) InvGamma(1, 0.15)
#> 15 Spike(0) InvGamma(1, 0.15)
#> 16 Spike(0) InvGamma(1, 0.15)
#> 17 Spike(0) InvGamma(1, 0.15)
#> 18 Spike(0) InvGamma(1, 0.15)
#> 19 Normal(0, 1) Spike(0)
#> 20 Normal(0, 1) Spike(0)
#> 21 Normal(0, 1) Spike(0)
#> 22 Normal(0, 1) Spike(0)
#> 23 Normal(0, 1) Spike(0)
#> 24 Normal(0, 1) Spike(0)
#> 25 Normal(0, 1) Spike(0)
#> 26 Normal(0, 1) Spike(0)
#> 27 Normal(0, 1) Spike(0)
#> 28 Normal(0, 1) InvGamma(1, 0.15)
#> 29 Normal(0, 1) InvGamma(1, 0.15)
#> 30 Normal(0, 1) InvGamma(1, 0.15)
#> 31 Normal(0, 1) InvGamma(1, 0.15)
#> 32 Normal(0, 1) InvGamma(1, 0.15)
#> 33 Normal(0, 1) InvGamma(1, 0.15)
#> 34 Normal(0, 1) InvGamma(1, 0.15)
#> 35 Normal(0, 1) InvGamma(1, 0.15)
#> 36 Normal(0, 1) InvGamma(1, 0.15)
#> Prior Bias Prior prob.
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031
#> 0.125
#> omega[two-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[two-sided: .1, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.010
#> omega[one-sided: .05, .025] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.010
#> omega[one-sided: .5, .05, .025] ~ CumDirichlet(1, 1, 1, 1) 0.010
#> PET ~ Cauchy(0, 1)[0, Inf] 0.031
#> PEESE ~ Cauchy(0, 5)[0, Inf] 0.031