bigSurvSGD.na.omit: Fit Survival Models with Stochastic Gradient Descent

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

Fit Survival Models with Stochastic Gradient Descent

Description

Performs stochastic gradient descent optimisation for large-scale survival models after removing observations with missing values.

Usage

bigSurvSGD.na.omit(
  formula = survival::Surv(time = time, status = status) ~ .,
  data,
  norm.method = "standardize",
  features.mean = NULL,
  features.sd = NULL,
  opt.method = "AMSGrad",
  beta.init = NULL,
  beta.type = "averaged",
  lr.const = 0.12,
  lr.tau = 0.5,
  strata.size = 20,
  batch.size = 1,
  num.epoch = 100,
  b1 = 0.9,
  b2 = 0.99,
  eps = 1e-08,
  inference.method = "plugin",
  num.boot = 1000,
  num.epoch.boot = 100,
  boot.method = "SGD",
  lr.const.boot = 0.12,
  lr.tau.boot = 0.5,
  num.sample.strata = 1000,
  sig.level = 0.05,
  beta0 = 0,
  alpha = NULL,
  lambda = NULL,
  nlambda = 100,
  num.strata.lambda = 10,
  lambda.scale = 1,
  parallel.flag = FALSE,
  num.cores = NULL,
  bigmemory.flag = FALSE,
  num.rows.chunk = 1e+06,
  col.names = NULL,
  type = "float"
)

Arguments

formula	Model formula describing the survival outcome and the set of predictors to include in the optimisation.
data	Input data set or connection to a big-memory backed design matrix that contains the variables referenced in formula.
norm.method	Normalization strategy applied to the feature matrix before optimisation, for example centring or standardising columns.
features.mean	Optional pre-computed column means used when normalising the features so that repeated fits can reuse shared statistics.
features.sd	Optional pre-computed column standard deviations used in concert with features.mean for scaling the predictors.
opt.method	Gradient based optimisation routine to employ, such as vanilla SGD or adaptive methods like Adam.
beta.init	Vector of starting values for the regression coefficients supplied when warm-starting the optimisation.
beta.type	Indicator controlling how beta.init is interpreted, for example whether the coefficients correspond to the original or normalised scale.
lr.const	Base learning-rate constant used by the stochastic gradient descent routine.
lr.tau	Learning-rate decay horizon or damping factor that moderates the step size schedule.
strata.size	Number of observations drawn per stratum when building mini-batches for the optimisation loop.
batch.size	Total number of observations assembled into each stochastic gradient batch.
num.epoch	Number of passes over the training data used during the optimisation.
b1	First exponential moving-average rate used by adaptive methods such as Adam to smooth gradients.
b2	Second exponential moving-average rate used by adaptive methods to smooth squared gradients.
eps	Numerical stabilisation constant added to denominators when updating the adaptive moments.
inference.method	Inference approach requested after fitting, for example naive asymptotics or bootstrap resampling.
num.boot	Number of bootstrap replicates to draw when inference.method relies on resampling.
num.epoch.boot	Number of optimisation epochs to run within each bootstrap replicate.
boot.method	Type of bootstrap scheme to apply, such as ordinary or stratified resampling.
lr.const.boot	Learning-rate constant used during bootstrap refits.
lr.tau.boot	Learning-rate decay factor applied during bootstrap refits.
num.sample.strata	Number of strata sampled without replacement during each bootstrap iteration when stratified resampling is selected.
sig.level	Significance level used when constructing confidence intervals or hypothesis tests.
beta0	Optional vector of coefficients under the null hypothesis when performing hypothesis tests.
alpha	Elastic-net mixing parameter controlling the relative weight of \ell_1 and \ell_2 regularisation penalties.
lambda	Sequence of regularisation strengths supplied explicitly for penalised estimation.
nlambda	Number of automatically generated lambda values when a grid is produced internally.
num.strata.lambda	Number of strata used when tuning lambda via cross-validation or other search procedures.
lambda.scale	Scale on which the lambda grid is generated, for example logarithmic or linear spacing.
parallel.flag	Logical flag enabling parallel computation of gradients or bootstrap replicates.
num.cores	Number of processing cores to use when parallel execution is enabled.
bigmemory.flag	Logical flag indicating whether intermediate matrices should be stored using bigmemory backed objects.
num.rows.chunk	Row chunk size to use when streaming data from an on-disk matrix representation.
col.names	Optional character vector of column names associated with the feature matrix.
type	Type of survival model to fit, for example Cox proportional hazards or accelerated failure time variants.

Value

A fitted model object storing the learned coefficients, optimisation metadata, and any requested inference summaries. coef: Log of hazards ratio. If no inference is used, it returns a vector for estimated coefficients: If inference is used, it returns a matrix including estimates and confidence intervals of coefficients. In case of penalization, it resturns a matrix with columns corresponding to lambdas. coef.exp: Exponentiated version of coef (hazards ratio). lambda: Returns lambda(s) used for penalizarion. alpha: Returns alpha used for penalizarion. features.mean: Returns means of features, if given or calculated features.sd: Returns standard deviations of features, if given or calculated.

See Also

See Also bigSurvSGD, bigscale for constructing normalised design matrices and partialbigSurvSGDv0 for partial fitting pipelines.

Examples

data(micro.censure, package = "bigPLScox")
surv_data <- stats::na.omit(micro.censure[, c("survyear", "DC", "sexe", "Agediag")])
# Increase num.epoch and num.boot for real use
fit <- bigSurvSGD.na.omit(
   survival::Surv(survyear, DC) ~ .,
   data = surv_data,
   norm.method = "standardize",
   opt.method = "adam",
   batch.size = 16,
   num.epoch = 2,
 )

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