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Prints summary of "RoBMA.reg" ensemble implied by the specified priors and formula

Source: R/check-input-and-settings.R
check_setup.reg.Rd

check_setup prints summary of "RoBMA.reg" ensemble implied by the specified prior distributions. It is useful for checking the ensemble configuration prior to fitting all of the models.

check_setup prints summary of "RoBMA.reg" 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.reg(
  formula,
  data,
  test_predictors = TRUE,
  study_names = NULL,
  study_ids = NULL,
  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
  standardize_predictors = TRUE,
  effect_direction = "positive",
  priors = NULL,
  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,
  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
  prior_covariates_null = prior("spike", parameters = list(location = 0)),
  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
    contrast = "meandif"),
  prior_factors_null = prior("spike", parameters = list(location = 0)),
  models = FALSE,
  silent = FALSE,
  ...
)

check_setup.RoBMA.reg(
  formula,
  data,
  test_predictors = TRUE,
  study_names = NULL,
  study_ids = NULL,
  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
  standardize_predictors = TRUE,
  effect_direction = "positive",
  priors = NULL,
  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,
  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
  prior_covariates_null = prior("spike", parameters = list(location = 0)),
  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
    contrast = "meandif"),
  prior_factors_null = prior("spike", parameters = list(location = 0)),
  models = FALSE,
  silent = FALSE,
  ...
)

check_setup.reg(
  formula,
  data,
  test_predictors = TRUE,
  study_names = NULL,
  study_ids = NULL,
  transformation = if (any(colnames(data) != "y")) "fishers_z" else "none",
  prior_scale = if (any(colnames(data) != "y")) "cohens_d" else "none",
  standardize_predictors = TRUE,
  effect_direction = "positive",
  priors = NULL,
  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,
  prior_covariates = prior("normal", parameters = list(mean = 0, sd = 0.25)),
  prior_covariates_null = prior("spike", parameters = list(location = 0)),
  prior_factors = prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25),
    contrast = "meandif"),
  prior_factors_null = prior("spike", parameters = list(location = 0)),
  models = FALSE,
  silent = FALSE,
  ...
)

Arguments

formula

a formula for the meta-regression model

data

a data.frame containing the data for the meta-regression. Note that the column names have to correspond to the effect sizes (d, logOR, OR, r, z), a measure of sampling variability (se, v, n, lCI, uCI, t), and the predictors. See combine_data() for a complete list of reserved names and additional information about specifying input data.

test_predictors

vector of predictor names to test for the presence of moderation (i.e., assigned both the null and alternative prior distributions). Defaults to TRUE, all predictors are tested using the default prior distributions (i.e., prior_covariates, prior_covariates_null, prior_factors, and prior_factors_null). To only estimate and adjust for the effect of predictors use FALSE. If priors is specified, any settings in test_predictors is overridden.

study_names

an optional argument with the names of the studies

study_ids

an optional argument specifying dependency between the studies (for using a multilevel model). Defaults to NULL for studies being independent.

transformation

transformation to be applied to the supplied effect sizes before fitting the individual models. Defaults to "fishers_z". We highly recommend using "fishers_z" transformation since it is the only variance stabilizing measure and does not bias PET and PEESE style models. The other options are "cohens_d", correlation coefficient "r" and "logOR". Supplying "none" will treat the effect sizes as unstandardized and refrain from any transformations.

prior_scale

an effect size scale used to define priors. Defaults to "cohens_d". Other options are "fishers_z", correlation coefficient "r", and "logOR". The prior scale does not need to match the effect sizes measure - the samples from prior distributions are internally transformed to match the transformation of the data. The prior_scale corresponds to the effect size scale of default output, but can be changed within the summary function.

standardize_predictors

whether continuous predictors should be standardized prior to estimating the model. Defaults to TRUE. Continuous predictor standardization is important for applying the default prior distributions for continuous predictors. Note that the resulting output corresponds to standardized meta-regression coefficients.

effect_direction

the expected direction of the effect. Correctly specifying the expected direction of the effect is crucial for one-sided selection models, as they specify cut-offs using one-sided p-values. Defaults to "positive" (another option is "negative").

priors

named list of prior distributions for each predictor (with names corresponding to the predictors). It allows users to specify both the null and alternative hypothesis prior distributions for each predictor by assigning the corresponding element of the named list with another named list (with "null" and "alt"). If only one prior is specified for a given parameter, it is assumed to correspond to the alternative hypotheses and the default null hypothesis is specified (i.e., prior_covariates_null or prior_factors_null). If a named list with only one named prior distribution is provided (either "null" or "alt"), only this prior distribution is used and no default distribution is filled in. Parameters without specified prior distributions are assumed to be only adjusted for using the default alternative hypothesis prior distributions (i.e., prior_covariates or prior_factors). If priors is specified, test_predictors is ignored.

model_type

string specifying the RoBMA ensemble. Defaults to NULL. The other options are "PSMA", "PP", and "2w" which override settings passed to the priors_effect, priors_heterogeneity, priors_effect, priors_effect_null, priors_heterogeneity_null, priors_bias_null, and priors_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 distribution prior(distribution = "normal", parameters = list(mean = 0, sd = 1)).

priors_heterogeneity

list of prior distributions for the heterogeneity tau parameter that will be treated as belonging to the alternative hypothesis. Defaults to prior(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 tau parameter 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 omega parameter 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 when study_ids are supplied. Note that this is an experimental feature and see News for more details. Defaults to a beta distribution prior(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 to NULL.

prior_covariates

a prior distributions for the regression parameter of continuous covariates on the effect size under the alternative hypothesis (unless set explicitly in priors). Defaults to a relatively wide normal distribution prior(distribution = "normal", parameters = list(mean = 0, sd = 0.25)).

prior_covariates_null

a prior distributions for the regression parameter of continuous covariates on the effect size under the null hypothesis (unless set explicitly in priors). Defaults to a no effect prior("spike", parameters = list(location = 0)).

prior_factors

a prior distributions for the regression parameter of categorical covariates on the effect size under the alternative hypothesis (unless set explicitly in priors). Defaults to a relatively wide multivariate normal distribution specifying differences from the mean contrasts prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25), contrast = "meandif").

prior_factors_null

a prior distributions for the regression parameter of categorical covariates on the effect size under the null hypothesis (unless set explicitly in priors). Defaults to a no effect prior("spike", parameters = list(location = 0)).

models

should the models' details be printed.

silent

do not print the results.

...

additional arguments.

Value

check_setup.reg invisibly returns list of summary tables.

check_setup.reg invisibly returns list of summary tables.

See also

check_setup() RoBMA.reg()

check_setup() RoBMA.reg()

Examples

# check regression setup with example data
data(Andrews2021)
check_setup.reg(~ measure + age, data = Andrews2021)
#> Warning: You are about to estimate 144 models based on the model formula and prior specification.
#> Robust Bayesian meta-regression (set-up)
#> Components summary:
#>                Models Prior prob.
#> Effect         72/144       0.500
#> Heterogeneity  72/144       0.500
#> Bias          128/144       0.500
#> 
#> Meta-regression components summary:
#>         Models Prior prob.
#> measure 72/144       0.500
#> age     72/144       0.500

# check setup with custom priors
check_setup.reg(~ measure + age, data = Andrews2021,
                priors_effect = prior("normal", list(mean = 0, sd = 0.5),
                prior_weight = 2))
#> Warning: You are about to estimate 144 models based on the model formula and prior specification.
#> Robust Bayesian meta-regression (set-up)
#> Components summary:
#>                Models Prior prob.
#> Effect         72/144       0.667
#> Heterogeneity  72/144       0.500
#> Bias          128/144       0.500
#> 
#> Meta-regression components summary:
#>         Models Prior prob.
#> measure 72/144       0.500
#> age     72/144       0.500