Glance accepts a model object and returns a `tibble::tibble()`

with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as `NA`

.

Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an `NA`

of the appropriate type.

## Usage

```
# S3 method for rma
glance(x, ...)
```

## 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()`

.- ...
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 exactly one row and columns:

- cochran.qe
In meta-analysis, test statistic for the Cochran's Q_e test of residual heterogeneity.

- cochran.qm
In meta-analysis, test statistic for the Cochran's Q_m omnibus test of coefficients.

- df.residual
Residual degrees of freedom.

- h.squared
Value of the H-Squared statistic.

- i.squared
Value of the I-Squared statistic.

- measure
The measure used in the meta-analysis.

- method
Which method was used.

- nobs
Number of observations used.

- p.value.cochran.qe
In meta-analysis, p-value for the Cochran's Q_e test of residual heterogeneity.

- p.value.cochran.qm
In meta-analysis, p-value for the Cochran's Q_m omnibus test of coefficients.

- tau.squared
In meta-analysis, estimated amount of residual heterogeneity.

- tau.squared.se
In meta-analysis, standard error of residual heterogeneity.

## Examples

```
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")
glance(meta_analysis)
#> # A tibble: 1 × 15
#> i.squared h.squared tau.squared tau.squared.se cochran.qe
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 92.3 13.0 0.318 0.174 152.
#> # ℹ 10 more variables: p.value.cochran.qe <dbl>, cochran.qm <dbl>,
#> # p.value.cochran.qm <dbl>, df.residual <int>, logLik <dbl>,
#> # deviance <dbl>, AIC <dbl>, BIC <dbl>, AICc <dbl>, nobs <int>
```