pycox_prepare_train_data {survivalmodels} | R Documentation |
Prepare Data for Pycox Model Training
Description
Utility function to prepare data for training in a Pycox model.
Generally used internally only.
Usage
pycox_prepare_train_data(
x_train,
y_train,
frac = 0,
standardize_time = FALSE,
log_duration = FALSE,
with_mean = TRUE,
with_std = TRUE,
discretise = FALSE,
cuts = 10L,
cutpoints = NULL,
scheme = c("equidistant", "quantiles"),
cut_min = 0L,
model = c("coxtime", "deepsurv", "deephit", "loghaz", "pchazard")
)
Arguments
x_train |
(matrix(1)) Training covariates.
|
y_train |
(matrix(1)) Training outcomes.
|
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 to be
standardized. For use with coxtime.
|
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.
|
discretise |
(logical(1)) If TRUE then time is discretised. For
use with the models
deephit, pchazard, and loghaz.
|
cuts |
(integer(1)) If discretise is TRUE then determines
number of cut-points for discretisation.
|
cutpoints |
(numeric()) Alternative to cuts if discretise is
true, provide exact cutpoints for discretisation. cuts is ignored if
cutpoints is non-NULL.
|
scheme |
(character(1)) Method of discretisation, either
"equidistant" (default) or "quantiles" . See
reticulate::py_help(pycox$models$LogisticHazard$label_transform) .
|
cut_min |
(integer(1)) Starting duration for discretisation, see
reticulate::py_help(pycox$models$LogisticHazard$label_transform) .
|
model |
(character(1)) Corresponding pycox model.
|
Value
No return value.
[Package
survivalmodels version 0.1.191
Index]