statsExpressions (version 0.3.1)

bf_meta: Bayes factor message for random-effects meta-analysis

Description

Bayes factor message for random-effects meta-analysis

Usage

bf_meta(
  data,
  d = prior("norm", c(mean = 0, sd = 0.3)),
  tau = prior("invgamma", c(shape = 1, scale = 0.15)),
  k = 2,
  output = "null",
  caption = NULL,
  messages = TRUE,
  ...
)

Arguments

data

data frame containing the variables for effect size y, standard error SE, labels, and moderators per study.

d

prior distribution on the average effect size d. The prior probability density function is defined via prior.

tau

prior distribution on the between-study heterogeneity tau (i.e., the standard deviation of the study effect sizes dstudy in a random-effects meta-analysis. A (nonnegative) prior probability density function is defined via prior.

k

Number of digits after decimal point (should be an integer) (Default: k = 2).

output

Character describing the desired output. If "subtitle", a formatted subtitle with summary effect and statistical details will be returned, and if "caption", expression containing details from model summary will be returned. The other option is to return "tidy" data frame with coefficients or "glance" dataframe with model summaries.

caption

Text to display as caption. This argument is relevant only when output = "caption".

messages

Decides whether messages references, notes, and warnings are to be displayed (Default: TRUE).

...

Arguments passed on to metaBMA::meta_random

labels

optional: character values with study labels. Can be a character vector or the quoted or unquoted name of the variable in data

rscale_contin

scale parameter of the JZS prior for the continuous covariates.

rscale_discrete

scale parameter of the JZS prior for discrete moderators.

centering

whether continuous moderators are centered.

logml

how to estimate the log-marginal likelihood: either by numerical integration ("integrate") or by bridge sampling using MCMC/Stan samples ("stan"). To obtain high precision with logml="stan", many MCMC samples are required (e.g., logml_iter=10000, warmup=1000).

summarize

how to estimate parameter summaries (mean, median, SD, etc.): Either by numerical integration (summarize = "integrate") or based on MCMC/Stan samples (summarize = "stan").

ci

probability for the credibility/highest-density intervals.

rel.tol

relative tolerance used for numerical integration using integrate. Use rel.tol=.Machine$double.eps for maximal precision (however, this might be slow).

logml_iter

number of iterations (per chain) from the posterior distribution of d and tau. The samples are used for computing the marginal likelihood of the random-effects model with bridge sampling (if logml="stan") and for obtaining parameter estimates (if summarize="stan"). Note that the argument iter=2000 controls the number of iterations for estimation of the random-effect parameters per study in random-effects meta-analysis.

silent_stan

whether to suppress the Stan progress bar.

Examples

# NOT RUN {
# }
# NOT RUN {
# setup
set.seed(123)
library(metaBMA)

# creating a dataframe
(df <-
  structure(
    .Data = list(
      study = c("1", "2", "3", "4", "5"),
      estimate = c(
        0.382047603321706,
        0.780783111514665,
        0.425607573765058,
        0.558365541235078,
        0.956473848429961
      ),
      std.error = c(
        0.0465576338644502,
        0.0330218199731529,
        0.0362834986178494,
        0.0480571500648261,
        0.062215818388157
      )
    ),
    row.names = c(NA, -5L),
    class = c("tbl_df", "tbl", "data.frame")
  ))

# getting Bayes factor in favor of null hypothesis
bf_meta(
  data = df,
  k = 3,
  iter = 1500,
  messages = TRUE,
  # customizing analysis with additional arguments
  control = list(max_treedepth = 15)
)
# }
# NOT RUN {
# }