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.

## Usage

```
# S3 method for rma
tidy(
x,
conf.int = FALSE,
conf.level = 0.95,
exponentiate = FALSE,
include_studies = FALSE,
measure = "GEN",
...
)
```

## Arguments

- x
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()`

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

`FALSE`

.- conf.level
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.- exponentiate
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`

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

`FALSE`

.- measure
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`

. Two exceptions here are:

## Value

A `tibble::tibble()`

with columns:

- conf.high
Upper bound on the confidence interval for the estimate.

- conf.low
Lower bound on the confidence interval for the estimate.

- estimate
The estimated value of the regression term.

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

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

- std.error
The standard error of the regression term.

- term
The name of the individual study

- type
The estimate type (summary vs individual study)

## Examples

```
# load libraries for models and data
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
```