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Calculate the SmoothGrad values of the Survival Function

surv_smoothgrad.Rd

This function calculates the SmoothGrad values of the survival function with respect to the input features and time points for a given instance. In the paper, this is referred to as the "SG(t)" method. It shows the smoothed sensitivity of the survival function to changes in the input features at a specific time point.

Usage

surv_smoothgrad(
  exp,
  target = "survival",
  instance = 1,
  times_input = FALSE,
  batch_size = 50,
  n = 10,
  noise_level = 0.1,
  dtype = "float",
  include_time = FALSE
)

# S3 method for class 'explainer_deepsurv'
surv_smoothgrad(
  exp,
  target = "survival",
  instance = 1,
  times_input = FALSE,
  batch_size = 1000,
  n = 10,
  noise_level = 0.1,
  dtype = "float",
  ...
)

# S3 method for class 'explainer_coxtime'
surv_smoothgrad(
  exp,
  target = "survival",
  instance = 1,
  times_input = FALSE,
  batch_size = 1000,
  n = 10,
  noise_level = 0.1,
  dtype = "float",
  include_time = FALSE
)

# S3 method for class 'explainer_deephit'
surv_smoothgrad(
  exp,
  target = "survival",
  instance = 1,
  times_input = FALSE,
  batch_size = 1000,
  n = 10,
  noise_level = 0.1,
  dtype = "float",
  ...
)

Arguments

exp

An object of class explainer_deepsurv, explainer_coxtime, or explainer_deephit.

target

A character string indicating the target output. For DeepSurv and CoxTime, it can be either "survival" (default), "cum_hazard", or "hazard". For DeepHit, it can be "survival" (default), "cif", or "pmf".

instance

An integer specifying the instance for which the SmoothGrad is calculated. It should be between 1 and the number of instances in the dataset.

times_input

A logical value indicating whether the SmoothGrad should be multiplied with input. In the paper, this variant is referred to as "SGxI(t)".

batch_size

An integer specifying the batch size for processing. The default is 1000. This value describes the number of instances within one batch and not the final number of rows in the batch. For example, CoxTime replicates each instance for each time point.

n

An integer specifying the number of noise samples to be added to the input features. The default is 10.

noise_level

A numeric value specifying the level of Gaussian noise to be added to the input features. The default is 0.1.

dtype

A character string indicating the data type for the tensors. It can be either "float" (default) or "double".

include_time

A logical value indicating whether to also calculate the gradients with respect to the time. This is only relevant for CoxTime and is ignored for DeepSurv and DeepHit.

...

Unused arguments.

See also

Other Attribution Methods: surv_gradSHAP(), surv_grad(), surv_intgrad()