Displays a concise summary of DER diagnostic results, including the DER range, classification threshold, number of flagged parameters, and correction status.
Usage
# S3 method for class 'svyder'
print(x, n = 10, digits = 3, ...)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
summary.svyder() for detailed classification output,
tidy.svyder() for a tidy data frame.
Other svyder-methods:
as.matrix.svyder(),
glance.svyder(),
is.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
)
print(result)
#> svyder diagnostic (54 parameters)
#> Family: binomial | N = 6785 | J = 51
#> Target: 644 cluster(s) | meat: centered, DF-corrected | weights: unit_mean
#> DER range: [0.235, 5.308]
#> Tier III (DER undefined): sigma_theta
#> Threshold (tau): 1.20
#> Flagged: 30 / 54 (55.6%)
#>
#> Flagged parameters:
#> beta[2] DER = 2.689 [I-a] -> CORRECT
#> theta[1] DER = 3.386 [II] -> CORRECT
#> theta[4] DER = 2.210 [II] -> CORRECT
#> theta[5] DER = 1.568 [II] -> CORRECT
#> theta[6] DER = 2.104 [II] -> CORRECT
#> theta[7] DER = 2.232 [II] -> CORRECT
#> theta[9] DER = 5.308 [II] -> CORRECT
#> theta[11] DER = 2.655 [II] -> CORRECT
#> theta[15] DER = 1.576 [II] -> CORRECT
#> theta[18] DER = 4.025 [II] -> CORRECT
#> ... and 20 more
#>
#> Correction applied: 30 parameter(s) rescaled
#> Compute time: 0.056 sec