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Print Method for DPprior_fit Objects

Source: R/10_a1_mapping.R, R/17_s3_methods.R
print.DPprior_fit.Rd

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, ...)

# S3 method for class 'DPprior_fit'
print(x, digits = 4, ...)

Arguments

x

A DPprior_fit object.

digits

Integer; number of significant digits for display. Default is 4.

...

Additional arguments (currently unused).

Value

Invisibly returns the input object.

Invisibly returns x.

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\

See also

summary.DPprior_fit, plot.DPprior_fit, DPprior_fit

Examples

fit <- DPprior_a1(J = 50, mu_K = 5, var_K = 8)
print(fit)
#> DPprior Prior Elicitation Result
#> ============================================= 
#> 
#> Gamma Hyperprior: α ~ Gamma(a = 4.0000, b = 3.9120)
#>   E[α] = 1.022, SD[α] = 0.511
#> 
#> Target (J = 50):
#>   E[K_J]   = 5.00
#>   Var(K_J) = 8.00
#> 
#> Method: A1 (0 iterations)

# 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 (RN-07).
#>   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%)