Fits a Cox partial-likelihood neural network through
survivalmodels::deepsurv(). This optional adapter requires a Python
environment containing torch, torchtuples, and pycox and currently
supports only uniform observation weights.
Usage
surv.deepsurv(
time,
event,
X,
newdata = NULL,
new.times,
obsWeights = NULL,
id = NULL,
num_nodes = c(32L, 32L),
activation = "relu",
batch_norm = TRUE,
dropout = NULL,
epochs = 100L,
batch_size = 128L,
device = NULL,
verbose = FALSE,
seed = 1L,
...
)Arguments
- time
Observed follow-up time.
- event
Observed event indicator.
- X
Training covariate data frame.
- newdata
Covariate data frame used for prediction.
- new.times
Times at which survival probabilities are requested.
- obsWeights
Optional non-negative observation weights.
- id
Currently ignored.
- num_nodes
Positive integers giving hidden-layer sizes.
- activation
Neural-network activation name.
- batch_norm
Whether to use batch normalization.
- dropout
Optional dropout probability in
[0, 1).- epochs
Number of training epochs.
- batch_size
Training and prediction batch size.
- device
Optional device passed to
survivalmodels.- verbose
Whether the Python backend should print training progress.
- seed
Positive integer used for Python, NumPy, and torch random-number generators.
- ...
Additional arguments passed to
survivalmodels::deepsurv().
Details
Fitted Python objects can be reused in the active R/Python session.
Plain saveRDS() is not a portable persistence format for these objects.
Native survival curves are mapped to requested times as right-continuous
steps; see surv.coxtime() for the boundary convention.
Examples
if (interactive() && requireNamespace("survivalmodels", quietly = TRUE) &&
requireNamespace("reticulate", quietly = TRUE) &&
reticulate::py_module_available("pycox")) {
data("metabric", package = "SuperSurv")
dat <- metabric[1:60, ]
X <- dat[, grep("^x", names(dat))[1:3], drop = FALSE]
fit <- surv.deepsurv(
dat$duration, dat$event, X, X[1:4, , drop = FALSE],
c(50, 100), epochs = 2, seed = 1
)
dim(fit$pred)
}