Summary of the object

The AUTO_VI$..str..() method provides a string representation of the object. If a check is performed, the string will contain some simple statistics of the check result. This method does this same thing as AUTO_VI$..str..(), but it returns an AUTO_VI_SUMMARY object which stores those statistics, such as sample quantiles of the distribution of null visual signal strength, in the object.

Usage

AUTO_VI$summary()

Value

An AUTO_VI_SUMMARY object.

Examples

keras_model <- try(get_keras_model("vss_phn_32"))
if (!inherits(keras_model, "try-error")) {
  myvi <- auto_vi(lm(dist ~ speed, data = cars), keras_model)

  myvi$check()
  myvi_summary <- myvi$summary()
  print(myvi_summary)
  names(myvi_summary)
}
#> ✔ Generate null data.
#> ✔ Generate null plots.
#> Computing auxiliary inputs ■■■■■■■■■■                        31% | ETA:  2s
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■■■■■■              64% | ETA:  1s
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Compute auxilary inputs.
#> Saving images ■■■■■■■■■■■■■■                    43% | ETA:  2s
#> Saving images ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Predict visual signal strength for 100 images.
#> ✔ Generate bootstrapped data.
#> ✔ Generate bootstrapped plots.
#> Warning: Problem in area computation (Returns NA)
#> Warning: triangle collapsed!
#> Warning: three points coincide or are collinear!
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■                   45% | ETA:  1s
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: Problem in area computation (Returns NA)
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Compute auxilary inputs.
#> Saving images ■■■■■■■■■■■■                      37% | ETA:  2s
#> Saving images ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Predict visual signal strength for 100 images.
#> ✔ Predict visual signal strength for 1 image.
#> 
#> ── <AUTO_VI_SUMMARY object>
#> Status:
#>  - Fitted model: lm
#>  - Keras model: (None, 32, 32, 3) + (None, 5) -> (None, 1)
#>     - Output node index: 1
#>  - Result:
#>     - Observed visual signal strength: 3.162 (p-value = 0.0495)
#>     - Null visual signal strength: [100 draws]
#>        - Mean: 1.469
#>        - Quantiles: 
#>           ╔═════════════════════════════════════════════════╗
#>           ║   25%    50%    75%    80%    90%    95%    99% ║
#>           ║0.9556 1.3271 1.7486 1.8066 2.3116 2.9083 3.4890 ║
#>           ╚═════════════════════════════════════════════════╝
#>     - Bootstrapped visual signal strength: [100 draws]
#>        - Mean: 2.682 (p-value = 0.06931)
#>        - Quantiles: 
#>           ╔══════════════════════════════════════════╗
#>           ║  25%   50%   75%   80%   90%   95%   99% ║
#>           ║2.314 2.839 3.269 3.316 3.463 3.544 3.600 ║
#>           ╚══════════════════════════════════════════╝
#>     - Likelihood ratio: 0.5769 (boot) / 0.05851 (null) = 9.861 
#>  [1] "null_likelihood"  "..method_env.."   "observed_vss"     "null_quantiles"  
#>  [5] "del_attr"         "..mro.."          "..repr.."         "has_attr"        
#>  [9] "boot_quantiles"   "null_draws"       "get_attr"         "boot_draws"      
#> [13] "..instantiated.." "..class_tree.."   "..init.."         "..methods.."     
#> [17] "boot_mean"        "boot_p_value"     "p_value"          "null_mean"       
#> [21] "..class.."        "..type.."         "..dir.."          "..bases.."       
#> [25] "boot_likelihood"  "..new.."          "..init_call.."    "set_attr"        
#> [29] "..str.."          "summary_string"   "likelihood_ratio" "..len.."