loghaz: Logistic-Hazard Survival Neural Network
In survivalmodels: Models for Survival Analysis

loghaz

R Documentation

Logistic-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

loghaz(
  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,
  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.LogisticHazard`, 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.LogisticHazard.fit`, elements in each batch.
`epochs`	`(integer(1))` Passed to `pycox.models.LogisticHazard.fit`, number of epochs.
`verbose`	`(logical(1))` Passed to `pycox.models.LogisticHazard.fit`, should information be displayed during fitting.
`num_workers`	`(integer(1))` Passed to `pycox.models.LogisticHazard.fit`, number of workers used in the dataloader.
`shuffle`	`(logical(1))` Passed to `pycox.models.LogisticHazard.fit`, should order of dataset be shuffled?
`...`	`ANY` Passed to get_pycox_optim.