CoxTime Survival Neural Network
Description
CoxTime fits a neural network based on the Cox PH with
possibly timedependent effects.
Usage
coxtime(
formula = NULL,
data = NULL,
reverse = FALSE,
time_variable = "time",
status_variable = "status",
x = NULL,
y = NULL,
frac = 0,
standardize_time = FALSE,
log_duration = FALSE,
with_mean = TRUE,
with_std = TRUE,
activation = "relu",
num_nodes = c(32L, 32L),
batch_norm = TRUE,
dropout = NULL,
device = NULL,
shrink = 0,
early_stopping = FALSE,
best_weights = FALSE,
min_delta = 0,
patience = 10L,
batch_size = 256L,
epochs = 1L,
verbose = FALSE,
num_workers = 0L,
shuffle = TRUE,
...
)
Arguments
formula 
(formula(1))
Object specifying the model fit, lefthandside of formula should describe a survival::Surv()
object.

data 
(data.frame(1))
Training data of data.frame like object, internally is coerced with stats::model.matrix() .

reverse 
(logical(1))
If TRUE fits estimator on censoring distribution, otherwise (default) survival distribution.

time_variable 
(character(1))
Alternative method to call the function. Name of the 'time' variable, required if formula .
or x and Y not given.

status_variable 
(character(1))
Alternative method to call the function. Name of the 'status' variable, required if formula
or x and Y not given.

x 
(data.frame(1))
Alternative method to call the function. Required if formula, time_variable and
status_variable not given. Data frame like object of features which is internally
coerced with model.matrix .

y 
([survival::Surv()])
Alternative method to call the function. Required if formula, time_variable and
status_variable not given. Survival outcome of rightcensored observations.

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 is standardized.

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.

activation 
(character(1))
See get_pycox_activation.

num_nodes , batch_norm , dropout 
(integer()/logical(1)/numeric(1))
See build_pytorch_net.

device 
(integer(1)character(1))
Passed to pycox.models.Coxtime , specifies device to compute models on.

shrink 
(numeric(1))
Passed to pycox.models.Coxtime , shrinkage parameter for regularization.

early_stopping , best_weights , min_delta , patience 
(logical(1)/logical(1)/numeric(1)/integer(1)
See get_pycox_callbacks.

batch_size 
(integer(1))
Passed to pycox.models.Coxtime.fit , elements in each batch.

epochs 
(integer(1))
Passed to pycox.models.Coxtime.fit , number of epochs.

verbose 
(logical(1))
Passed to pycox.models.Coxtime.fit , should information be displayed during
fitting.

num_workers 
(integer(1))
Passed to pycox.models.Coxtime.fit , number of workers used in the
dataloader.

shuffle 
(logical(1))
Passed to pycox.models.Coxtime.fit , should order of dataset be shuffled?

... 
ANY
Passed to get_pycox_optim.

Details
Implemented from the pycox
Python package via reticulate.
Calls pycox.models.Coxtime
.
Value
An object inheriting from class coxtime
.
An object of class survivalmodel
.
References
Kvamme, H., Borgan, Ø., & Scheel, I. (2019).
Timetoevent prediction with neural networks and Cox regression.
Journal of Machine Learning Research, 20(129), 1–30.