orsf_pd_oob: ORSF partial dependence

In aorsf: Accelerated Oblique Random Survival Forests

ORSF partial dependence

Description

Compute partial dependence for an ORSF model. Partial dependence (PD) shows the expected prediction from a model as a function of a single predictor or multiple predictors. The expectation is marginalized over the values of all other predictors, giving something like a multivariable adjusted estimate of the model's prediction. You can compute partial dependence three ways using a random forest:

• using in-bag predictions for the training data

• using out-of-bag predictions for the training data

• using predictions for a new set of data

See examples for more details

Usage

orsf_pd_oob(
  object,
  pred_spec,
  pred_horizon = NULL,
  pred_type = "risk",
  expand_grid = TRUE,
  prob_values = c(0.025, 0.5, 0.975),
  prob_labels = c("lwr", "medn", "upr"),
  boundary_checks = TRUE,
  n_thread = 1,
  ...
)

orsf_pd_inb(
  object,
  pred_spec,
  pred_horizon = NULL,
  pred_type = "risk",
  expand_grid = TRUE,
  prob_values = c(0.025, 0.5, 0.975),
  prob_labels = c("lwr", "medn", "upr"),
  boundary_checks = TRUE,
  n_thread = 1,
  ...
)

orsf_pd_new(
  object,
  pred_spec,
  new_data,
  pred_horizon = NULL,
  pred_type = "risk",
  na_action = "fail",
  expand_grid = TRUE,
  prob_values = c(0.025, 0.5, 0.975),
  prob_labels = c("lwr", "medn", "upr"),
  boundary_checks = TRUE,
  n_thread = 1,
  ...
)

Arguments

object	(orsf_fit) a trained oblique random survival forest (see orsf).
pred_spec	(named list or data.frame). If pred_spec is a named list, Each item in the list should be a vector of values that will be used as points in the partial dependence function. The name of each item in the list should indicate which variable will be modified to take the corresponding values. If pred_spec is a data.frame, columns will indicate variable names, values will indicate variable values, and partial dependence will be computed using the inputs on each row.
pred_horizon	(double) a value or vector indicating the time(s) that predictions will be calibrated to. E.g., if you were predicting risk of incident heart failure within the next 10 years, then pred_horizon = 10. pred_horizon can be NULL if pred_type is 'mort', since mortality predictions are aggregated over all event times
pred_type	(character) the type of predictions to compute. Valid options are 'risk' : probability of having an event at or before pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction
expand_grid	(logical) if TRUE, partial dependence will be computed at all possible combinations of inputs in pred_spec. If FALSE, partial dependence will be computed for each variable in pred_spec, separately.
prob_values	(numeric) a vector of values between 0 and 1, indicating what quantiles will be used to summarize the partial dependence values at each set of inputs. prob_values should have the same length as prob_labels. The quantiles are calculated based on predictions from object at each set of values indicated by pred_spec.
prob_labels	(character) a vector of labels with the same length as prob_values, with each label indicating what the corresponding value in prob_values should be labelled as in summarized outputs. prob_labels should have the same length as prob_values.
boundary_checks	(logical) if TRUE, pred_spec will be checked to make sure the requested values are between the 10th and 90th percentile in the object's training data. If FALSE, these checks are skipped.
n_thread	(integer) number of threads to use while computing predictions. Default is one thread. To use the maximum number of threads that your system provides for concurrent execution, set n_thread = 0.
...	Further arguments passed to or from other methods (not currently used).
new_data	a data.frame, tibble, or data.table to compute predictions in.
na_action	(character) what should happen when new_data contains missing values (i.e., NA values). Valid options are: 'fail' : an error is thrown if new_data contains NA values 'omit' : rows in new_data with incomplete data will be dropped

Details

Partial dependence has a number of known limitations and assumptions that users should be aware of (see Hooker, 2021). In particular, partial dependence is less intuitive when >2 predictors are examined jointly, and it is assumed that the feature(s) for which the partial dependence is computed are not correlated with other features (this is likely not true in many cases). Accumulated local effect plots can be used (see here) in the case where feature independence is not a valid assumption.

Value

a data.table containing partial dependence values for the specified variable(s) at the specified prediction horizon(s).

Examples

Begin by fitting an ORSF ensemble:

library(aorsf)

set.seed(329730)

index_train <- sample(nrow(pbc_orsf), 150) 

pbc_orsf_train <- pbc_orsf[index_train, ]
pbc_orsf_test <- pbc_orsf[-index_train, ]

fit <- orsf(data = pbc_orsf_train, 
            formula = Surv(time, status) ~ . - id,
            oobag_pred_horizon = 365.25 * 5)

Three ways to compute PD and ICE

You can compute partial dependence and ICE three ways with aorsf:

• using in-bag predictions for the training data

  pd_train <- orsf_pd_inb(fit, pred_spec = list(bili = 1:5))
  
  pd_train
  

  ##    pred_horizon bili     mean      lwr      medn      upr
  ## 1:      1826.25    1 101.9466 10.53944  51.65470 387.5041
  ## 2:      1826.25    2 118.2382 16.90238  65.95072 400.6156
  ## 3:      1826.25    3 138.9013 27.34446  91.45440 408.3768
  ## 4:      1826.25    4 163.9056 46.18300 121.82058 417.1405
  ## 5:      1826.25    5 181.6854 62.99029 140.65257 418.7087
  

• using out-of-bag predictions for the training data

  pd_train <- orsf_pd_oob(fit, pred_spec = list(bili = 1:5))
  
  pd_train
  

  ##    pred_horizon bili     mean       lwr     medn      upr
  ## 1:      1826.25    1 37.66151  3.607834 20.75476 137.8659
  ## 2:      1826.25    2 43.56664  6.655814 25.94357 143.4564
  ## 3:      1826.25    3 51.04030 10.102343 33.73989 146.7145
  ## 4:      1826.25    4 60.28418 17.159042 43.95543 148.7279
  ## 5:      1826.25    5 66.74464 22.974170 53.00352 149.4068
  

• using predictions for a new set of data

  pd_test <- orsf_pd_new(fit, 
                         new_data = pbc_orsf_test, 
                         pred_spec = list(bili = 1:5))
  
  pd_test
  

  ##    pred_horizon bili     mean      lwr     medn      upr
  ## 1:      1826.25    1 121.9552 10.86471  88.9915 402.0936
  ## 2:      1826.25    2 137.8436 19.81224 107.7018 411.1170
  ## 3:      1826.25    3 159.1454 31.76190 134.2937 418.7824
  ## 4:      1826.25    4 184.4209 52.09751 162.6736 427.0102
  ## 5:      1826.25    5 202.1922 69.21315 179.9189 428.3857
  

• in-bag partial dependence indicates relationships that the model has learned during training. This is helpful if your goal is to interpret the model.

• out-of-bag partial dependence indicates relationships that the model has learned during training but using the out-of-bag data simulates application of the model to new data. if you want to test your model’s reliability or fairness in new data but you don’t have access to a large testing set.

• new data partial dependence shows how the model predicts outcomes for observations it has not seen. This is helpful if you want to test your model’s reliability or fairness.

References

Giles Hooker, Lucas Mentch, Siyu Zhou. Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance. arXiv e-prints 2021 Oct; arXiv-1905. URL: https://doi.org/10.48550/arXiv.1905.03151

aorsf documentation built on Oct. 26, 2023, 5:08 p.m.