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ggstatsplot (version 0.1.4)

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

Description

Bayes factor message for random-effects meta-analysis

Usage

bf_meta_message(
  data,
  k = 2,
  d = "norm",
  d.par = c(mean = 0, sd = 0.3),
  tau = "halfcauchy",
  tau.par = c(scale = 0.5),
  iter = 10000,
  summarize = "stan",
  caption = NULL,
  messages = TRUE,
  ...
)

Arguments

data

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

k

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

d

the prior distribution of the average effect size d specified either as the type of family (e.g., "norm") or via prior.

d.par

prior parameters for d (only used if d specifies the type of family).

tau

the prior distribution of the between-study heterogeneity τ specified either as a character value (e.g., "halfcauchy") or via prior.

tau.par

prior parameters for τ (only used if tau specifies the type of family).

iter

number of MCMC iterations using Stan.

summarize

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

caption

The text for the plot caption.

messages

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

...

further arguments passed to rstan::sampling (see stanmodel-method-sampling). Relevant MCMC settings concern the number of warmup samples that are discarded (warmup=500), the total number of iterations per chain (iter=2000), the number of MCMC chains (chains=4), whether multiple cores should be used (cores=4), and control arguments that make the sampling in Stan more robust, for instance: control=list(adapt_delta=.97).

Examples

Run this code
# 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
ggstatsplot::bf_meta_message(
  data = df,
  k = 3,
  iter = 1500,
  messages = TRUE
)
# }
# NOT RUN {
# }

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