deephit: DeepHit Survival Neural Network

In survivalmodels: Models for Survival Analysis

DeepHit Survival Neural Network

Description

DeepHit fits a neural network based on the PMF of a discrete Cox model. This is the single (non-competing) event implementation.

Usage

deephit(
  formula = NULL,
  data = NULL,
  reverse = FALSE,
  time_variable = "time",
  status_variable = "status",
  x = NULL,
  y = NULL,
  frac = 0,
  cuts = 10,
  cutpoints = NULL,
  scheme = c("equidistant", "quantiles"),
  cut_min = 0,
  activation = "relu",
  custom_net = NULL,
  num_nodes = c(32L, 32L),
  batch_norm = TRUE,
  dropout = NULL,
  device = NULL,
  mod_alpha = 0.2,
  sigma = 0.1,
  early_stopping = FALSE,
  best_weights = FALSE,
  min_delta = 0,
  patience = 10L,
  batch_size = 256L,
  epochs = 1L,
  verbose = FALSE,
  num_workers = 0L,
  shuffle = TRUE,
  ...
)

Arguments

formula	(formula(1)) Object specifying the model fit, left-hand-side of formula should describe a survival::Surv() object.
data	(data.frame(1)) Training data of data.frame like object, internally is coerced with stats::model.matrix().
reverse	(logical(1)) If TRUE fits estimator on censoring distribution, otherwise (default) survival distribution.
time_variable	(character(1)) Alternative method to call the function. Name of the 'time' variable, required if formula. or x and Y not given.
status_variable	(character(1)) Alternative method to call the function. Name of the 'status' variable, required if formula or x and Y not given.
x	(data.frame(1)) Alternative method to call the function. Required if ⁠formula, time_variable⁠ and status_variable not given. Data frame like object of features which is internally coerced with model.matrix.
y	⁠([survival::Surv()])⁠ Alternative method to call the function. Required if ⁠formula, time_variable⁠ and status_variable not given. Survival outcome of right-censored observations.
frac	(numeric(1)) Fraction of data to use for validation dataset, default is 0 and therefore no separate validation dataset.
cuts	(integer(1)) If discretise is TRUE then determines number of cut-points for discretisation.
cutpoints	(numeric()) Alternative to cuts if discretise is true, provide exact cutpoints for discretisation. cuts is ignored if cutpoints is non-NULL.
scheme	(character(1)) Method of discretisation, either "equidistant" (default) or "quantiles". See reticulate::py_help(pycox$models$LogisticHazard$label_transform) for more detail.
cut_min	(integer(1)) Starting duration for discretisation, see reticulate::py_help(pycox$models$LogisticHazard$label_transform) for more detail.
activation	(character(1)) See get_pycox_activation.
custom_net	(torch.nn.modules.module.Module(1)) Optional custom network built with build_pytorch_net, otherwise default architecture used. Note that if building a custom network the number of output channels depends on cuts or cutpoints.
num_nodes, batch_norm, dropout	(integer()/logical(1)/numeric(1)) See build_pytorch_net.
device	(integer(1)|character(1)) Passed to pycox.models.DeepHitSingle, specifies device to compute models on.
mod_alpha	(numeric(1)) Weighting in (0,1) for combining likelihood (L1) and rank loss (L2). See reference and py_help(pycox$models$DeepHitSingle) for more detail.
sigma	(numeric(1)) From eta in rank loss (L2) of ref. See reference and py_help(pycox$models$DeepHitSingle) for more detail.
early_stopping, best_weights, min_delta, patience	⁠(logical(1)/logical(1)/numeric(1)/integer(1)⁠ See get_pycox_callbacks.
batch_size	(integer(1)) Passed to pycox.models.DeepHitSingle.fit, elements in each batch.
epochs	(integer(1)) Passed to pycox.models.DeepHitSingle.fit, number of epochs.
verbose	(logical(1)) Passed to pycox.models.DeepHitSingle.fit, should information be displayed during fitting.
num_workers	(integer(1)) Passed to pycox.models.DeepHitSingle.fit, number of workers used in the dataloader.
shuffle	(logical(1)) Passed to pycox.models.DeepHitSingle.fit, should order of dataset be shuffled?
...	ANY Passed to get_pycox_optim.

Details

Implemented from the pycox Python package via reticulate. Calls pycox.models.DeepHitSingle.

Value

An object inheriting from class deephit.

An object of class survivalmodel.

References

Changhee Lee, William R Zame, Jinsung Yoon, and Mihaela van der Schaar. Deephit: A deep learning approach to survival analysis with competing risks. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. http://medianetlab.ee.ucla.edu/papers/AAAI_2018_DeepHit

survivalmodels documentation built on May 29, 2024, 7:26 a.m.