Glance at a(n) svyglm 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 'svyglm'
glance(x, maximal = x, ...)

Arguments

x

A svyglm object returned from survey::svyglm().

maximal

A svyglm object corresponding to the maximal model against which to compute the BIC. See Lumley and Scott (2015) for details. Defaults to x, which is equivalent to not using a maximal model.

...

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.

References

Lumley T, Scott A (2015). AIC and BIC for modelling with complex survey data. Journal of Survey Statistics and Methodology, 3(1).

See also

survey::svyglm(), stats::glm(), survey::anova.svyglm

Other lm tidiers: augment.glm(), augment.lm(), glance.glm(), glance.lm(), glance.summary.lm(), tidy.glm(), tidy.lm(), tidy.lm.beta(), tidy.mlm(), tidy.summary.lm()

Value

A tibble::tibble() with exactly one row and columns:

AIC

Akaike's Information Criterion for the model.

BIC

Bayesian Information Criterion for the model.

deviance

Deviance of the model.

df.null

Degrees of freedom used by the null model.

df.residual

Residual degrees of freedom.

null.deviance

Deviance of the null model.

Examples

# load libraries for models and data
library(survey)
#> Loading required package: grid
#> 
#> Attaching package: ‘survey’
#> The following object is masked from ‘package:drc’:
#> 
#>     twophase
#> The following object is masked from ‘package:graphics’:
#> 
#>     dotchart

set.seed(123)
data(api)

# survey design
dstrat <-
  svydesign(
    id = ~1,
    strata = ~stype,
    weights = ~pw,
    data = apistrat,
    fpc = ~fpc
  )

# model
m <- svyglm(
  formula = sch.wide ~ ell + meals + mobility,
  design = dstrat,
  family = quasibinomial()
)

glance(m)
#> # A tibble: 1 × 7
#>   null.deviance df.null   AIC   BIC deviance df.residual  nobs
#>           <dbl>   <int> <dbl> <dbl>    <dbl>       <dbl> <int>
#> 1          184.     199  184.  199.     178.         194   200