pycox_prepare_train_data: Prepare Data for Pycox Model Training
In survivalmodels: Models for Survival Analysis

pycox_prepare_train_data

R Documentation

Prepare Data for Pycox Model Training

Description

Utility function to prepare data for training in a Pycox model. Generally used internally only.

Usage

pycox_prepare_train_data(
  x_train,
  y_train,
  frac = 0,
  standardize_time = FALSE,
  log_duration = FALSE,
  with_mean = TRUE,
  with_std = TRUE,
  discretise = FALSE,
  cuts = 10L,
  cutpoints = NULL,
  scheme = c("equidistant", "quantiles"),
  cut_min = 0L,
  model = c("coxtime", "deepsurv", "deephit", "loghaz", "pchazard")
)

Arguments

`x_train`	`(matrix(1))` Training covariates.
`y_train`	`(matrix(1))` Training outcomes.
`frac`	`(numeric(1))` Fraction of data to use for validation dataset, default is `0` and therefore no separate validation dataset.
`standardize_time`	`(logical(1))` If `TRUE`, the time outcome to be standardized. For use with coxtime.
`log_duration`	`(logical(1))` If `TRUE` and `standardize_time` is `TRUE` then time variable is log transformed.
`with_mean`	`(logical(1))` If `TRUE` (default) and `standardize_time` is `TRUE` then time variable is centered.
`with_std`	`(logical(1))` If `TRUE` (default) and `standardize_time` is `TRUE` then time variable is scaled to unit variance.
`discretise`	`(logical(1))` If `TRUE` then time is discretised. For use with the models deephit, pchazard, and loghaz.
`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$label_transform)`.
`cut_min`	`(integer(1))` Starting duration for discretisation, see `reticulate::py_help(pycox$models$LogisticHazard$label_transform)`.
`model`	`(character(1))` Corresponding pycox model.