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Extract information about the fitted prognostic model

Source: R/rctglm_prog_methods.R
prog.Rd

Extracts the prognostic_info list element from an rctglm_prog object. See 'Value' at rctglm_with_prognosticscore for more details.

Usage

prog(x)

# S3 method for class 'rctglm_prog'
prog(x)

Arguments

x

an object of class rctglm_prog (returned by rctglm_with_prognosticscore)

Value

a list with the structure described of prognostic_info in the Value section of rctglm_with_prognosticscore.

See also

The generic rctglm_with_prognosticscore() for which this method works.

Examples

# Generate some data
n <- 100
b0 <- 1
b1 <- 1.5
b2 <- 2
W1 <- runif(n, min = 1, max = 10)
exposure_prob <- .5

dat_treat <- glm_data(
  Y ~ b0+b1*log(W1)+b2*A,
  W1 = W1,
  A = rbinom(n, 1, exposure_prob)
)

dat_notreat <- glm_data(
  Y ~ b0+b1*log(W1),
  W1 = W1
)

learners <- list(
  mars = list(
    model = parsnip::set_engine(
      parsnip::mars(
        mode = "regression", prod_degree = 3
      ),
      "earth"
    )
  )
)
ate <- rctglm_with_prognosticscore(
  formula = Y ~ .,
  exposure_indicator = A,
  exposure_prob = exposure_prob,
  data = dat_treat,
  family = gaussian(),
  estimand_fun = "ate",
  data_hist = dat_notreat,
  learners = learners)
#> 
#> ── Fitting prognostic model ──
#> 
#> ℹ Created formula for fitting prognostic model as: Y ~ .
#> ℹ Fitting learners
#> • mod_mars
#> i	No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 1 resampling: mod_mars
#> ✔ 1 of 1 resampling: mod_mars (297ms)
#> ℹ Model with lowest RMSE: mod_mars
#> ℹ Investigate trained learners and fitted model in `prognostic_info` list element
#> 
#> ── Symbolic differentiation of estimand function ──
#> 
#> ℹ Symbolically deriving partial derivative of the function 'psi1 - psi0' with respect to 'psi0' as: '-1'.
#> • Alternatively, specify the derivative through the argument
#> `estimand_fun_deriv0`
#> ℹ Symbolically deriving partial derivative of the function 'psi1 - psi0' with respect to 'psi1' as: '1'.
#> • Alternatively, specify the derivative through the argument
#> `estimand_fun_deriv1`

prog(ate)
#> $formula
#> Y ~ .
#> <environment: 0x561fcedd9120>
#> 
#> $model_fit
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: mars()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> Y ~ .
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Selected 2 of 8 terms, and 1 of 1 predictors
#> Termination condition: RSq changed by less than 0.001 at 8 terms
#> Importance: W1
#> Number of terms at each degree of interaction: 1 1 (additive model)
#> GCV 1.135246    RSS 105.7169    GRSq 0.282691    RSq 0.3184613
#> 
#> $learners
#> $learners$mars
#> $learners$mars$model
#> MARS Model Specification (regression)
#> 
#> Main Arguments:
#>   prod_degree = 3
#> 
#> Computational engine: earth 
#> 
#> 
#> 
#> 
#> $cv_folds
#> [1] 5
#> 
#> $data
#>              Y       W1
#> 1    3.6621214 4.104007
#> 2    4.8931947 8.282395
#> 3    2.4137504 4.080676
#> 4    5.7968296 9.948056
#> 5    4.3081985 8.054280
#> 6    2.5380249 7.743522
#> 7    1.6429072 3.100132
#> 8    2.3105753 4.175262
#> 9    2.1871016 8.202766
#> 10   3.7518964 8.146120
#> 11   5.9150520 9.287358
#> 12   1.3699347 1.978866
#> 13   3.7591325 4.584301
#> 14   5.1650980 8.229323
#> 15   4.3320181 4.734298
#> 16  -0.5007689 1.723683
#> 17   3.3452634 8.362255
#> 18   0.9292991 3.768794
#> 19   4.9152530 6.755241
#> 20   1.0954642 1.077221
#> 21   2.4474979 4.141598
#> 22   4.6433913 8.628211
#> 23   4.1404019 6.778669
#> 24   6.1254019 8.987460
#> 25   4.5214864 9.603369
#> 26   4.0504724 3.703488
#> 27   4.5763059 8.374734
#> 28   4.3357868 4.729989
#> 29   3.3444561 3.750651
#> 30   4.1465791 5.665703
#> 31   2.1578178 7.521980
#> 32   1.9609490 1.326455
#> 33   3.3738200 9.518249
#> 34   3.3538673 5.185436
#> 35   2.6333517 9.018702
#> 36   2.4942810 4.915578
#> 37   3.8007426 3.997709
#> 38   2.9718932 8.508268
#> 39   4.8894148 4.411281
#> 40   4.5558673 8.595222
#> 41   4.6488715 8.007686
#> 42   3.9956681 7.781198
#> 43   4.2139132 7.055589
#> 44   3.9063102 3.587769
#> 45   4.4792488 9.796029
#> 46   4.7076808 7.450139
#> 47   2.5515069 3.410406
#> 48   1.2758184 1.270140
#> 49   2.4536496 6.023307
#> 50   3.6064859 4.992764
#> 51   4.0638982 6.226682
#> 52   3.5000614 4.404522
#> 53   3.3961279 3.656227
#> 54   3.2624292 8.140726
#> 55   2.4579680 3.575334
#> 56   5.1745566 9.625719
#> 57   3.1324650 4.661983
#> 58   4.9610940 9.516887
#> 59   3.7286589 7.067554
#> 60   4.2697903 3.931327
#> 61   3.6815286 7.265746
#> 62   5.5700792 9.508500
#> 63   5.1648382 5.678420
#> 64   1.8103229 2.647777
#> 65   4.7291421 9.258796
#> 66   1.8635523 3.707809
#> 67   2.5569000 1.890157
#> 68   5.3924140 9.694692
#> 69   5.3828237 7.690062
#> 70   4.2042933 2.429835
#> 71   4.5110811 3.409121
#> 72   2.4829185 4.966140
#> 73   4.7295659 9.106231
#> 74   3.1687328 3.879241
#> 75   2.1894319 5.245921
#> 76   2.8204314 2.613819
#> 77   3.9642139 6.715504
#> 78   3.8718682 8.996986
#> 79   4.3989075 8.250813
#> 80   2.4347479 5.188651
#> 81   3.3879994 6.797406
#> 82   3.7085525 7.321683
#> 83   2.9047902 5.802903
#> 84   3.1441057 8.895249
#> 85   2.9483110 9.480564
#> 86   2.2639374 2.476536
#> 87   3.2728221 4.507343
#> 88   2.5205627 2.574187
#> 89   2.0209244 3.831910
#> 90   3.5027177 7.287288
#> 91   3.1018521 6.522201
#> 92   4.9502369 8.963631
#> 93   1.8117292 4.244663
#> 94   6.2061094 7.727389
#> 95   4.7863444 1.854713
#> 96   2.5202512 8.859881
#> 97   3.7115434 3.699677
#> 98   3.7682327 7.864654
#> 99   3.4832679 4.238855
#> 100  2.4147340 7.063403
#>