S3 method for printing cv_model results

S3 method for printing cv_model results

# S3 method for bmbstats_cv_model
print(x, ...)

Arguments

x	Object of class bmbstats_cv_model
...	Extra arguments

Examples

data("vertical_jump_data")

m1 <- cv_model(
  `Post-test` ~ `Pre-test` * Group * `Squat 1RM`,
  vertical_jump_data,
  control = model_control(
    cv_repeats = 10,
    cv_folds = 3,
    cv_strata = vertical_jump_data$Group
  )
)
#> Cross-validating: 3 folds, 10 repeats
#> Done!

m1
#> Training data consists of 8 predictors and 30 observations. Cross-Validation of the model was performed using 10 repeats of 3 folds.
#> 
#> Model performance:
#> 
#>         metric      training training.pooled testing.pooled       mean
#>            MBE -1.563192e-14    3.884316e-15      0.2311052  0.2311052
#>            MAE  8.763836e-01    7.306604e-01      1.4379213  1.4379213
#>           RMSE  1.059620e+00    9.166555e-01      1.9037243  1.8547725
#>           PPER  7.787868e-01    8.510785e-01      0.5125763  0.5055777
#>  SESOI to RMSE  2.543190e+00    2.892834e+00      1.3929184  1.5211796
#>      R-squared  9.744091e-01    9.808487e-01      0.9186148  0.9113843
#>         MinErr -2.117309e+00   -2.459842e+00     -3.9171978 -2.5194035
#>         MaxErr  1.706648e+00    1.902532e+00      9.1259564  3.5829687
#>      MaxAbsErr  2.117309e+00    2.459842e+00      9.1259564  3.9383175
#>          SD        min        max
#>  0.56371177 -1.0633326  1.2602194
#>  0.25275572  0.9338355  2.0781957
#>  0.43626768  1.1205181  3.2501994
#>  0.08878779  0.2804820  0.7199031
#>  0.32733904  0.7847265  2.3910227
#>  0.06103823  0.6946005  0.9822198
#>  0.91286013 -3.9171978 -0.8861356
#>  1.87116370  0.8756370  9.1259564
#>  1.63899801  2.0690027  9.1259564
plot(m1, "residuals")