Glance accepts a model object and returns a
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
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
of the appropriate type.
# S3 method for rma glance(x, ...)
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
tibble::tibble() with exactly one row and columns:
In meta-analysis, test statistic for the Cochran's Q_e test of residual heterogeneity.
In meta-analysis, test statistic for the Cochran's Q_m omnibus test of coefficients.
Residual degrees of freedom.
Value of the H-Squared statistic.
Value of the I-Squared statistic.
The measure used in the meta-analysis.
Which method was used.
Number of observations used.
In meta-analysis, p-value for the Cochran's Q_e test of residual heterogeneity.
In meta-analysis, p-value for the Cochran's Q_m omnibus test of coefficients.
In meta-analysis, estimated amount of residual heterogeneity.
In meta-analysis, standard error of residual heterogeneity.
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 x 15 #> i.squared h.squared tau.squared tau.squared.se cochran.qe p.value.cochran.qe #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 92.3 13.0 0.318 0.174 152. 2.00e-26 #> # … with 9 more variables: cochran.qm <dbl>, p.value.cochran.qm <dbl>, #> # df.residual <int>, logLik <dbl>, deviance <dbl>, AIC <dbl>, BIC <dbl>, #> # AICc <dbl>, nobs <int>