Displays a concise, informative summary of a prior elicitation result, including the Gamma hyperprior specification, target vs achieved fit, and dominance risk assessment.
Usage
# S3 method for class 'DPprior_fit'
print(x, digits = 4, ...)Details
The output includes:
Gamma hyperprior parameters (a, b) with moments Escales::alpha and SDscales::alpha
Target specification (J, \(E[K_J]\), \(Var(K_J)\))
Achieved fit with residual error
Method used and iteration count
Quick dominance risk summary (if diagnostics available)
Dominance Risk
If diagnostics are computed, the dominance risk is displayed as:
LOW: P(w1 > 0.5) < 20\
MODERATE: 20\
HIGH: P(w1 > 0.5) >= 40\
Examples
# Create a fit object
fit <- DPprior_fit(J = 50, mu_K = 5, var_K = 8)
#> Warning: HIGH DOMINANCE RISK: P(w1 > 0.5) = 48.1% exceeds 40%.
#> This may indicate unintended prior behavior (Lee, 2026).
#> Consider using DPprior_dual() for weight-constrained elicitation.
#> See ?DPprior_diagnostics for interpretation.
print(fit)
#> DPprior Prior Elicitation Result
#> =============================================
#>
#> Gamma Hyperprior: α ~ Gamma(a = 2.0361, b = 1.6051)
#> E[α] = 1.269, SD[α] = 0.889
#>
#> Target (J = 50):
#> E[K_J] = 5.00
#> Var(K_J) = 8.00
#>
#> Achieved:
#> E[K_J] = 5.000000, Var(K_J) = 8.000000
#> Residual = 7.60e-09
#>
#> Method: A2-MN (6 iterations)
#>
#> Dominance Risk: HIGH ✘ (P(w₁>0.5) = 48%)
# With custom digits
print(fit, digits = 6)
#> DPprior Prior Elicitation Result
#> =============================================
#>
#> Gamma Hyperprior: α ~ Gamma(a = 2.036093, b = 1.605054)
#> E[α] = 1.269, SD[α] = 0.889
#>
#> Target (J = 50):
#> E[K_J] = 5.00
#> Var(K_J) = 8.00
#>
#> Achieved:
#> E[K_J] = 5.00000000, Var(K_J) = 8.00000001
#> Residual = 7.60e-09
#>
#> Method: A2-MN (6 iterations)
#>
#> Dominance Risk: HIGH ✘ (P(w₁>0.5) = 48%)