pchazard: PC-Hazard Survival Neural Network

In RaphaelS1/survivalmodels: Models for Survival Analysis

PC-Hazard Survival Neural Network

Description

Logistic-Hazard fits a discrete neural network based on a cross-entropy loss and predictions of a discrete hazard function, also known as Nnet-Survival.

Usage

pchazard(
  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,
  reduction = c("mean", "none", "sum"),
  dropout = NULL,
  device = NULL,
  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.
reduction	(character(1)) How to reduce the loss, see to reticulate::py_help(pycox$models$loss$NLLPCHazardLoss).
device	(integer(1)|character(1)) Passed to pycox.models.PCHazard, specifies device to compute models on.
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.PCHazard.fit, elements in each batch.
epochs	(integer(1)) Passed to pycox.models.PCHazard.fit, number of epochs.
verbose	(logical(1)) Passed to pycox.models.PCHazard.fit, should information be displayed during fitting.
num_workers	(integer(1)) Passed to pycox.models.PCHazard.fit, number of workers used in the dataloader.
shuffle	(logical(1)) Passed to pycox.models.PCHazard.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.PCHazard.

Value

An object inheriting from class pchazard.

An object of class survivalmodel.

References

Kvamme, H., & Borgan, Ø. (2019). Continuous and discrete-time survival prediction with neural networks. https://doi.org/arXiv:1910.06724.

Examples

if (requireNamespaces("reticulate")) {
  # all defaults
  pchazard(data = simsurvdata(50))

  # common parameters
  pchazard(data = simsurvdata(50), frac = 0.3, activation = "relu",
    num_nodes = c(4L, 8L, 4L, 2L), dropout = 0.1, early_stopping = TRUE, epochs = 100L,
    batch_size = 32L)
}

RaphaelS1/survivalmodels documentation built on July 5, 2024, 10:17 a.m.