Fit RISDR to real high-dimensional data

Fit RISDR to real high-dimensional data

Usage

fit_risdr_sparse_realdata(
  X,
  y,
  delta = NULL,
  response_type = c("continuous", "binary", "multiclass", "categorical", "survival"),
  variance_quantile = 0.25,
  ...
)

Arguments

X

Predictor matrix.

y

Response vector.

delta

Optional survival censoring indicator.

response_type

Response type.

variance_quantile

Variance filtering threshold.

...

Additional arguments passed to fit_risdr_sparse().

Value

Fitted RISDR workflow object. Fit RISDR to a real-data predictor matrix

Applies outcome cleaning, low-variance screening, the unified RISDR core, and descriptive loading-based variable ranking. The wrapper returns the reduced predictors computed by the core itself so that centring and standardisation are not silently bypassed.

"multiclass", "categorical", or "survival". low-variance screen.

An object of class risdr_realdata.

Details

This entry point uses the frozen sparse thesis engine. The existing fit_risdr() and fit_risdr_realdata() interfaces retain their continuous-response behaviour from version 0.3.1. Survival fits use Efron Cox partial likelihood. Apparent fit statistics and loading ranks are exploratory and do not establish external predictive validity.