predict.pycox {survivalmodels}

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.