Complexity-aware cross-validation for structural dimension selection
Source:R/cv_icomp.R
select_dimension_cv_icomp.RdCombines out-of-fold prediction error with rescaled BIC, CAIC, or CICOMP
penalties. The tuning constant lambda controls the contribution of the
information-criterion component.
Usage
select_dimension_cv_icomp(
X,
y,
sdr_method = c("dr", "sir", "save", "phd"),
cov_method = c("sample", "ridge", "oas", "lw", "mec"),
d_max = 10,
v = 5,
nslices = 6,
standardize = TRUE,
stabilize = TRUE,
stabilization = c("eigenfloor", "ridge", "nearest_pd"),
complexity = c("C1", "C1F"),
lambda = 1,
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.
- v
Number of cross-validation folds.
- nslices
Number of response slices.
- standardize
Logical. Standardise within each training fold.
- stabilize
Logical. Stabilise the estimated covariance matrix.
- stabilization
Covariance stabilisation method.
- complexity
Covariance complexity measure.
- lambda
Non-negative information-criterion weight.
- 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.