tune_Lambda {lgspline} | R Documentation |
Tune Smoothing and Ridge Penalties via Generalized Cross Validation
Description
Optimizes smoothing spline and ridge regression penalties by minimizing GCV criterion. Uses BFGS optimization with analytical gradients or finite differences.
Usage
tune_Lambda(
y,
X,
X_gram,
smoothing_spline_penalty,
A,
K,
nc,
nr,
opt,
use_custom_bfgs,
C,
colnm_expansions,
wiggle_penalty,
flat_ridge_penalty,
invsoftplus_initial_wiggle,
invsoftplus_initial_flat,
unique_penalty_per_predictor,
unique_penalty_per_partition,
invsoftplus_penalty_vec,
meta_penalty,
family,
unconstrained_fit_fxn,
keep_weighted_Lambda,
iterate,
qp_score_function,
quadprog,
qp_Amat,
qp_bvec,
qp_meq,
tol,
sd_y,
delta,
constraint_value_vectors,
parallel,
parallel_eigen,
parallel_trace,
parallel_aga,
parallel_matmult,
parallel_unconstrained,
cl,
chunk_size,
num_chunks,
rem_chunks,
shared_env,
custom_penalty_mat,
order_list,
glm_weight_function,
shur_correction_function,
need_dispersion_for_estimation,
dispersion_function,
observation_weights,
homogenous_weights,
blockfit,
just_linear_without_interactions,
Vhalf,
VhalfInv,
verbose,
include_warnings,
...
)
Arguments
y |
List; response vectors by partition |
X |
List; design matrices by partition |
X_gram |
List; Gram matrices by partition |
smoothing_spline_penalty |
Matrix; integrated squared second derivative penalty |
A |
Matrix; smoothness constraints at knots |
K |
Integer; number of interior knots in 1-D, number of partitions - 1 in higher dimensions |
nc |
Integer; columns per partition |
nr |
Integer; total sample size |
opt |
Logical; TRUE to optimize penalties, FALSE to use initial values |
use_custom_bfgs |
Logical; TRUE for analytic gradient BFGS as natively implemented, FALSE for finite differences as implemented by |
wiggle_penalty , flat_ridge_penalty |
Initial penalty values |
invsoftplus_initial_wiggle , invsoftplus_initial_flat |
Initial grid search values (log scale) |
unique_penalty_per_predictor , unique_penalty_per_partition |
Logical; allow predictor/partition-specific penalties |
invsoftplus_penalty_vec |
Initial values for predictor/partition penalties (log scale) |
meta_penalty |
The "meta" ridge penalty, a regularization for predictor/partition penalties to pull them on log-scale towards 0 (1 on raw scale) |
family |
GLM family with optional custom tuning loss |
keep_weighted_Lambda , iterate |
Logical controlling GLM fitting |
qp_score_function , quadprog , qp_Amat , qp_bvec , qp_meq |
Quadratic programming parameters (see arguments of |
tol |
Numeric; convergence tolerance |
sd_y , delta |
Response standardization parameters |
constraint_value_vectors |
List; constraint values |
parallel |
Logical; enable parallel computation |
cl , chunk_size , num_chunks , rem_chunks |
Parallel computation parameters |
custom_penalty_mat |
Optional custom penalty matrix |
order_list |
List; observation ordering by partition |
glm_weight_function , shur_correction_function |
Functions for GLM weights and corrections |
need_dispersion_for_estimation , dispersion_function |
Control dispersion estimation |
observation_weights |
Optional observation weights |
homogenous_weights |
Logical; TRUE if all weights equal |
blockfit |
Logical; when TRUE, block-fitting (not per-partition fitting) approach is used, analogous to quadratic programming. |
just_linear_without_interactions |
Numeric; vector of columns of input predictor matrix that correspond to non-spline effects without interactions, used for block-fitting. |
Vhalf , VhalfInv |
Square root and inverse square root correlation structures for fitting GEEs. |
verbose |
Logical; print progress |
include_warnings |
Logical; print warnings/try-errors |
... |
Additional arguments passed to fitting functions |
Details
Uses BFGS optimization to minimize GCV criterion for penalty selection. Supports analytical gradients for efficiency with standard GLM families. Can optimize unique penalties per predictor/partition. Handles custom loss functions and GLM weights. Parallel computation available for large problems.
Value
List containing:
Lambda - Final combined penalty matrix
flat_ridge_penalty - Optimized ridge penalty
wiggle_penalty - Optimized smoothing penalty
other_penalties - Optimized predictor/partition penalties
L_predictor_list - Predictor-specific penalty matrices
L_partition_list - Partition-specific penalty matrices
See Also
-
optim
for Hessian-free optimization