Glance at a(n) rma object

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 class '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:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

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>