Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for rma
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  include_studies = FALSE,
  measure = "GEN",



An rma object such as those created by metafor::rma(), metafor::rma.uni(), metafor::rma.glmm(), metafor::rma.mh(), metafor::rma.mv(), or metafor::rma.peto().


Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.


The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.


Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.


Logical. Should individual studies be included in the output? Defaults to FALSE.


Measure type. See metafor::escalc()


Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.


A tibble::tibble() with columns:


Upper bound on the confidence interval for the estimate.


Lower bound on the confidence interval for the estimate.


The estimated value of the regression term.


The two-sided p-value associated with the observed statistic.


The value of a T-statistic to use in a hypothesis that the regression term is non-zero.


The standard error of the regression term.


The name of the individual study


The estimate type (summary vs individual study)


library(metafor) df <- escalc( measure = "RR", ai = tpos, bi = tneg, ci = cpos, di = cneg, data = dat.bcg ) meta_analysis <- rma(yi, vi, data = df, method = "EB") tidy(meta_analysis)
#> # A tibble: 1 × 6 #> term type estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 overall summary -0.715 0.181 -3.95 0.0000774