predict.pycox: Predict Method for pycox Neural Networks

In survivalmodels: Models for Survival Analysis

Predict Method for pycox Neural Networks

Description

Predicted values from a fitted pycox ANN.

Usage

## S3 method for class 'pycox'
predict(
  object,
  newdata,
  batch_size = 256L,
  num_workers = 0L,
  interpolate = FALSE,
  inter_scheme = c("const_hazard", "const_pdf"),
  sub = 10L,
  type = c("survival", "risk", "all"),
  ...
)

Arguments

object	(pycox(1)) Object of class inheriting from "pycox".
newdata	(data.frame(1)) Testing data of data.frame like object, internally is coerced with stats::model.matrix(). If missing then training data from fitted object is used.
batch_size	(integer(1)) Passed to pycox.models.X.fit, elements in each batch.
num_workers	(integer(1)) Passed to pycox.models.X.fit, number of workers used in the dataloader.
interpolate	(logical(1)) For models deephit and loghaz, should predictions be linearly interpolated? Ignored for other models.
inter_scheme	(character(1)) If interpolate is TRUE then the scheme for interpolation, see reticulate::py_help(py_help(pycox$models$DeepHitSingle$interpolate)) for further details.
sub	(integer(1)) If interpolate is TRUE or model is loghaz, number of sub-divisions for interpolation. See reticulate::py_help(py_help(pycox$models$DeepHitSingle$interpolate))' for further details.
type	(character(1)) Type of predicted value. Choices are survival probabilities over all time-points in training data ("survival") or a relative risk ranking ("risk"), which is the negative mean survival time so higher rank implies higher risk of event, or both ("all").
...	ANY Currently ignored.

Value

A numeric if type = "risk", a matrix if type = "survival" where entries are survival probabilities with rows of observations and columns are time-points.

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