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 svyglm glance(x, maximal = x, ...)
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
Lumley T, Scott A (2015). AIC and BIC for modelling with complex survey data. Journal of Survey Statistics and Methodology, 3(1).
tibble::tibble() with exactly one row and columns:
Akaike's Information Criterion for the model.
Bayesian Information Criterion for the model.
Deviance of the model.
Degrees of freedom used by the null model.
Residual degrees of freedom.
Deviance of the null model.
#>#> #>#>#> #>#>#> #>set.seed(123) data(api) # survey design dstrat <- svydesign( id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc ) # model m <- survey::svyglm( formula = sch.wide ~ ell + meals + mobility, design = dstrat, family = quasibinomial() ) glance(m)#> # A tibble: 1 × 6 #> null.deviance df.null AIC BIC deviance df.residual #> <dbl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 184. 199 184. 199. 178. 194