bigscale: Construct Scaled Design Matrices for Big Survival Models

In bigPLScox: Partial Least Squares for Cox Models with Big Matrices

Construct Scaled Design Matrices for Big Survival Models

Description

Prepares a large-scale feature matrix for stochastic gradient descent byapplying optional normalisation, stratified sampling, and batching rules.

Usage

bigscale(
  formula = survival::Surv(time = time, status = status) ~ .,
  data,
  norm.method = "standardize",
  strata.size = 20,
  batch.size = 1,
  features.mean = NULL,
  features.sd = NULL,
  parallel.flag = FALSE,
  num.cores = NULL,
  bigmemory.flag = FALSE,
  num.rows.chunk = 1e+06,
  col.names = NULL,
  type = "short"
)

Arguments

formula	formula used to extract the outcome and predictors that should be included in the scaled design matrix.
data	Input data source containing the variables referenced in formula.
norm.method	Normalisation strategy (for example centring or standardising columns) applied to the feature matrix.
strata.size	Number of observations to retain from each stratum when constructing stratified batches.
batch.size	Total size of each mini-batch produced by the scaling routine.
features.mean	Optional vector of column means that can be reused to normalise multiple data sets in a consistent manner.
features.sd	Optional vector of column standard deviations that pairs with features.mean during scaling.
parallel.flag	Logical flag signalling whether the scaling work should be parallelised across cores.
num.cores	Number of processor cores allocated when parallel.flag is TRUE.
bigmemory.flag	Logical flag specifying whether intermediate results should be stored in bigmemory-backed matrices.
num.rows.chunk	Chunk size used when streaming data from on-disk objects into memory.
col.names	Optional character vector assigning column names to the generated design matrix.
type	Type of model or preprocessing target being prepared, such as survival or regression.

Value

A scaled design matrix of the scaler class along with metadata describing the transformation that was applied. time.indices: indices of the time variable cens.indices: indices of the censored variables features.indices: indices of the features time.sd: standard deviation of the time variable time.mean: mean of the time variable features.sd: standard deviation of the features features.mean: mean of the features nr: number of rows nc: number of columns col.names: columns names

See Also

bigSurvSGD.na.omit() for fitting models that use the scaled features.

Examples

data(micro.censure, package = "bigPLScox")
surv_data <- stats::na.omit(
  micro.censure[, c("survyear", "DC", "sexe", "Agediag")]
)
scaled <- bigscale(
  survival::Surv(survyear, DC) ~ .,
  data = surv_data,
  norm.method = "standardize",
  batch.size = 16
)
 

bigPLScox documentation built on Nov. 18, 2025, 5:06 p.m.