Returns a one-row data frame with model-level summary statistics following broom conventions. Provides an overview of the DER diagnostic results.
Usage
# S3 method for class 'svyder'
glance(x, ...)Value
A data.frame with one row and columns:
- n_params
Total number of parameters.
- n_flagged
Number of parameters flagged for correction.
- pct_flagged
Percentage of parameters flagged.
- tau
Classification threshold.
- family
Model family (
"binomial"or"gaussian").- n_obs
Number of observations.
- n_groups
Number of groups/clusters.
- mean_deff
Mean per-group design effect.
- mean_B
Mean per-group shrinkage factor.
- der_min
Minimum DER value.
- der_max
Maximum DER value.
References
Lee, J., Williams, M. R., & Savitsky, T. D. (2026). Design Effect Ratios for Bayesian Survey Models: A Diagnostic Framework for Identifying Survey-Sensitive Parameters. Journal of Survey Statistics and Methodology. Submitted.
See also
tidy.svyder() for per-parameter summaries.
Other svyder-methods:
as.matrix.svyder(),
is.svyder(),
print.svyder(),
summary.svyder(),
tidy.svyder()
Examples
data(nsece_demo)
result <- der_diagnose(
nsece_demo$draws,
y = nsece_demo$y, X = nsece_demo$X,
group = nsece_demo$group, weights = nsece_demo$weights,
cluster = nsece_demo$psu, family = "binomial",
sigma_theta = nsece_demo$sigma_theta,
param_types = nsece_demo$param_types
)
glance(result)
#> n_params n_flagged pct_flagged tau family n_obs n_groups mean_deff
#> 1 54 30 55.55556 1.2 binomial 6785 51 2.59527
#> mean_B der_min der_max
#> 1 0.8543053 0.2354213 5.307734