Summary method for an object of class "missRanger".
Usage
# S3 method for class 'missRanger'
summary(object, ...)Examples
CO2_ <- generateNA(CO2, seed = 1)
imp <- missRanger(CO2_, pmm.k = 5, data_only = FALSE, num.threads = 1)
#> Missing value imputation by random forests
#>
#> Variables to impute: Plant, Type, Treatment, conc, uptake
#> Variables used to impute: Plant, Type, Treatment, conc, uptake
#>
#> iter 1
#>
|
| | 0%
|
|============== | 20%
|
|============================ | 40%
|
|========================================== | 60%
|
|======================================================== | 80%
|
|======================================================================| 100%
#> iter 2
#>
|
| | 0%
|
|============== | 20%
|
|============================ | 40%
|
|========================================== | 60%
|
|======================================================== | 80%
|
|======================================================================| 100%
#> iter 3
#>
|
| | 0%
|
|============== | 20%
|
|============================ | 40%
|
|========================================== | 60%
|
|======================================================== | 80%
|
|======================================================================| 100%
summary(imp)
#> missRanger object. Extract imputed data via $data
#> - best iteration: 2
#> - best average OOB imputation error: 0.303332
#>
#> Sequence of OOB prediction errors:
#>
#> Plant Type Treatment conc uptake
#> [1,] 1.0000000 0.02631579 0.03947368 1.1114272 0.19045686
#> [2,] 0.7500000 0.00000000 0.00000000 0.6678399 0.09881985
#> [3,] 0.7368421 0.00000000 0.00000000 0.7309611 0.08577502
#>
#> Mean performance per iteration:
#> [1] 0.4735347 0.3033320 0.3107157
#>
#> First rows of imputed data:
#>
#> Plant Type Treatment conc uptake
#> 1 Qn2 Quebec nonchilled 95 16.0
#> 2 Qn1 Quebec nonchilled 175 30.4
#> 3 Qn1 Quebec nonchilled 250 34.8