Computes a simple ladle-type diagnostic by combining normalised eigenvalue information with a bootstrap-based subspace instability measure.
Usage
select_dimension_ladle(
X,
y,
sdr_method = c("dr", "sir", "save", "phd"),
cov_method = c("sample", "ridge", "oas", "lw", "mec"),
d_max = 10,
B = 100,
nslices = 6,
standardize = TRUE,
stabilize = TRUE,
stabilization = c("eigenfloor", "ridge", "nearest_pd"),
seed = NULL,
cov_args = list(),
stabilization_args = list(),
sdr_args = list(),
...
)Arguments
- X
Numeric predictor matrix or data frame.
- y
Numeric continuous response vector.
- sdr_method
SDR method.
- cov_method
Covariance estimator.
- d_max
Maximum candidate structural dimension.
- B
Number of bootstrap replications.
- nslices
Number of slices.
- standardize
Logical. If TRUE, standardises predictors.
- stabilize
Logical. If TRUE, stabilises covariance matrix.
- stabilization
Stabilisation method.
- seed
Optional random seed.
- cov_args
Named list of covariance-estimator arguments.
- stabilization_args
Named list of stabilisation arguments.
- sdr_args
Named list of SDR-kernel arguments.
- ...
Backward-compatible component arguments.