S3 method for printing RCT_predict results

Source: R/rct-predict-S3.R

S3 method for printing RCT_predict results

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

Arguments

x

Object of class bmbstats_RCT_predict
...

Extra arguments. Not used

Examples

data("vertical_jump_data")

m1 <- cv_model(
  `Post-test` ~ `Pre-test` + Group,
  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_rct <- RCT_predict(
  m1,
  new_data = vertical_jump_data,
  outcome = "Post-test",
  group = "Group",
  treatment_label = "Treatment",
  control_label = "Control"
)

m1_rct
#> Training data consists of 3 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 -7.342304e-15   -3.718485e-15     -0.0804454 -0.0804454
#>            MAE  2.410129e+00    2.379582e+00      2.7824510  2.7824510
#>           RMSE  3.314293e+00    3.207164e+00      3.8348030  3.6996958
#>           PPER  3.077021e-01    3.202917e-01      0.2697899  0.2838745
#>  SESOI to RMSE  8.130894e-01    8.268153e-01      0.6914912  0.8135316
#>      R-squared  7.496390e-01    7.655623e-01      0.6649729  0.6897063
#>         MinErr -7.076575e+00   -8.369427e+00     -8.7990381 -5.7301641
#>         MaxErr  8.795609e+00    9.960685e+00     11.3918573  5.8722501
#>      MaxAbsErr  8.795609e+00    9.960685e+00     11.3918573  7.7946851
#>         SD        min         max
#>  1.6346763 -3.4772322  3.22283737
#>  0.7365765  1.2780682  4.00023556
#>  1.0261909  1.6667244  5.01630511
#>  0.1110023  0.1592281  0.55673703
#>  0.3364630  0.4806244  1.69046764
#>  0.1679314  0.3532905  0.94067071
#>  2.7566454 -8.7990381 -0.07646052
#>  3.3123319 -0.5696611 11.39185726
#>  2.2187002  3.0889519 11.39185726
#> 
#> Individual model results:
#> 
#>  subject     group observed predicted     residual  magnitude counterfactual
#>        1   Control 44.28819  42.04167 -2.246520188      Lower       49.88950
#>        2   Control 47.98205  46.07303 -1.909018621      Lower       53.92086
#>        3   Control 45.13136  43.80419 -1.327164513 Equivalent       51.65202
#>        4   Control 48.85697  48.37440 -0.482576173 Equivalent       56.22222
#>        5   Control 42.12570  40.74050 -1.385202381      Lower       48.58833
#>        6   Control 41.60806  41.81824  0.210179844 Equivalent       49.66606
#>        7   Control 45.69844  45.08679 -0.611653379 Equivalent       52.93462
#>        8   Control 42.89175  43.79421  0.902458750 Equivalent       51.64204
#>        9   Control 42.15402  42.05525 -0.098768933 Equivalent       49.90308
#>       10   Control 37.83235  37.62593 -0.206420330 Equivalent       45.47376
#>       11   Control 34.83170  36.63302  1.801323154     Higher       44.48085
#>       12   Control 39.88302  39.88062 -0.002402407 Equivalent       47.72844
#>       13   Control 38.34326  39.16084  0.817582171 Equivalent       47.00867
#>       14   Control 36.96744  38.80457  1.837133658     Higher       46.65240
#>       15   Control 34.15168  36.85273  2.701049347     Higher       44.70056
#>       16 Treatment 52.86402  45.78745 -7.076574931      Lower       37.93962
#>       17 Treatment 58.50489  51.88463 -6.620263046      Lower       44.03680
#>       18 Treatment 59.76192  53.65842 -6.103493730      Lower       45.81060
#>       19 Treatment 49.58421  46.60804 -2.976168124      Lower       38.76021
#>       20 Treatment 51.41375  48.75083 -2.662918492      Lower       40.90300
#>       21 Treatment 57.57113  55.12834 -2.442796297      Lower       47.28051
#>       22 Treatment 51.41158  51.90657  0.494991154 Equivalent       44.05874
#>       23 Treatment 44.94979  45.03811  0.088313055 Equivalent       37.19028
#>       24 Treatment 52.72907  53.59372  0.864643040 Equivalent       45.74589
#>       25 Treatment 47.38184  50.07579  2.693952355     Higher       42.22797
#>       26 Treatment 54.11023  56.24927  2.139040280     Higher       48.40144
#>       27 Treatment 41.63256  45.73852  4.105951767     Higher       37.89069
#>       28 Treatment 45.41064  48.51731  3.106671448     Higher       40.66948
#>       29 Treatment 46.72143  52.31447  5.593042297     Higher       44.46664
#>       30 Treatment 45.61997  54.41558  8.795609224     Higher       46.56775
#>       pITE pITE_magnitude
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>   7.847828         Higher
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#>  -7.847828          Lower
#> 
#> Summary of residuals per RCT group:
#> 
#>      group          mean       SD
#>    Control -9.947512e-15 1.426115
#>  Treatment -4.736662e-15 4.637293
#> 
#> Summary of counterfactual effects of RCT group:
#> 
#>      group      pATE pVTE
#>  Treatment -7.847828    0
#>    Control  7.847828    0
#>     pooled  7.847828    0
#> 
#> Treatment effect summary
#> 
#> Average Treatment effect:  7.847828
#> Variable Treatment effect:  0
#> Random Treatment effect:  4.412559
plot(m1_rct)