Predict Method for pycox Neural Networks — predict.pycox • survivalmodels

Predicted values from a fitted pycox ANN.

# S3 method for 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"),
  distr6 = FALSE,
  ...
)

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").
distr6: (logical(1))
If FALSE (default) and type is "survival" or "all" returns matrix of survival probabilities, otherwise returns a distr6::Matdist().
...: ANY
Currently ignored.

Value

A numeric if type = "risk", a distr6::Matdist() (if distr6 = TRUE) and type = "survival"; a matrix if (distr6 = FALSE) and type = "survival" where entries are survival probabilities with rows of observations and columns are time-points; or a list combining above if type = "all".

Examples

if (FALSE) {
if (requireNamespaces("reticulate")) {
  fit <- coxtime(data = simsurvdata(50))

  # predict survival matrix and relative risks
  predict(fit, simsurvdata(10), type = "all")

  # return as distribution
  if (requireNamespaces("distr6")) {
    predict(fit, simsurvdata(10), distr6 = TRUE)
  }
}
}