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. Seecombine_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
, andprior_factors_null
). To only estimate and adjust for the effect of predictors useFALSE
. Ifpriors
is specified, any settings intest_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 thetransformation
of the data. Theprior_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
.- 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
orprior_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
orprior_factors
). Ifpriors
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 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
tau
parameter 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 erp2017estimatesRoBMA.- 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 bartos2021no;textualRoBMA.- 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 whenstudy_ids
are 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
.- 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 distributionprior(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 effectprior("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 contrastsprior_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 effectprior("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.