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Diagnose Revision Quality

Source: R/revisions.R
diagnose.Rd

Generic function to provide diagnostic summaries for revision analysis objects.

Usage

diagnose(object, ...)

Arguments

object

An object for which diagnostics are desired.

...

Additional arguments passed to methods.

Value

Method-specific diagnostic output.

See also

Other revision analysis: diagnose.revision_summary(), get_first_efficient_release(), get_revision_analysis(), print.lst_efficient(), print.revision_summary(), summary.lst_efficient(), summary.revision_summary()

Examples

# Example usage with revision analysis results
df <- dplyr::select(
  get_nth_release(
    na.omit(
      tsbox::ts_pc(
        dplyr::filter(reviser::gdp, id == "US")
      )
    ),
    n = 0:3
  ),
  -"pub_date"
)

final_release <- dplyr::select(
  get_nth_release(
    na.omit(
      tsbox::ts_pc(
        dplyr::filter(reviser::gdp, id == "US")
      )
    ),
    n = "latest"
  ),
  -"pub_date"
)

# Get revision analysis results
results <- get_revision_analysis(df, final_release, degree = 5)

# Diagnose revision quality
diagnose(results)
#> 
#> === Revision Quality Diagnostics ===
#> 
#> US_release_0 :
#> # A tibble: 6 × 4
#>   Metric        Status Value            Assessment                     
#>   <chr>         <chr>  <chr>            <chr>                          
#> 1 Unbiasedness  ✓ PASS p=0.255, μ=0.023 No significant bias            
#> 2 Noise/Signal  ✓ GOOD 0.27             Low revision volatility        
#> 3 News Test     ✗ FAIL p=0.002          Contains systematic information
#> 4 Noise Test    ✓ PASS p=0.372          No noise component             
#> 5 Theil's U1    ✓ GOOD 0.115            Good forecast accuracy         
#> 6 Sign Accuracy ✓ GOOD 94.9%            Excellent sign prediction      
#> 
#> US_release_1 :
#> # A tibble: 6 × 4
#>   Metric        Status Value           Assessment                     
#>   <chr>         <chr>  <chr>           <chr>                          
#> 1 Unbiasedness  ✓ PASS p=0.288, μ=0.02 No significant bias            
#> 2 Noise/Signal  ✓ GOOD 0.257           Low revision volatility        
#> 3 News Test     ✗ FAIL p=0             Contains systematic information
#> 4 Noise Test    ✓ PASS p=0.447         No noise component             
#> 5 Theil's U1    ✓ GOOD 0.11            Good forecast accuracy         
#> 6 Sign Accuracy ✓ GOOD 96%             Excellent sign prediction      
#> 
#> US_release_2 :
#> # A tibble: 6 × 4
#>   Metric        Status Value            Assessment                     
#>   <chr>         <chr>  <chr>            <chr>                          
#> 1 Unbiasedness  ✓ PASS p=0.238, μ=0.022 No significant bias            
#> 2 Noise/Signal  ✓ GOOD 0.26             Low revision volatility        
#> 3 News Test     ✗ FAIL p=0.001          Contains systematic information
#> 4 Noise Test    ✓ PASS p=0.342          No noise component             
#> 5 Theil's U1    ✓ GOOD 0.111            Good forecast accuracy         
#> 6 Sign Accuracy ✓ GOOD 95.5%            Excellent sign prediction      
#> 
#> US_release_3 :
#> # A tibble: 6 × 4
#>   Metric        Status Value            Assessment                     
#>   <chr>         <chr>  <chr>            <chr>                          
#> 1 Unbiasedness  ✓ PASS p=0.053, μ=0.032 No significant bias            
#> 2 Noise/Signal  ✓ GOOD 0.252            Low revision volatility        
#> 3 News Test     ✗ FAIL p=0              Contains systematic information
#> 4 Noise Test    ✓ PASS p=0.103          No noise component             
#> 5 Theil's U1    ✓ GOOD 0.108            Good forecast accuracy         
#> 6 Sign Accuracy ✓ GOOD 95.4%            Excellent sign prediction      
#> 
#> === Overall Assessment ===
#> Passed: 20 of 24 checks ( 83.3 %)
#> Overall: ✓ GOOD - Revisions are of high quality