Compute Sliced Average Variance Estimation
Usage
compute_save(
X,
y,
Sigma = NULL,
nslices = 6,
slice_type = c("quantile", "equal_width"),
stabilize_slices = TRUE,
stabilization = c("eigenfloor", "ridge", "nearest_pd"),
eps = 1e-08
)
Arguments
- X
Numeric predictor matrix.
- y
Numeric response vector.
- Sigma
Covariance matrix used for standardisation.
- nslices
Number of response slices.
- slice_type
Slicing strategy.
- stabilize_slices
Logical. Stabilise slice covariance matrices.
- stabilization
Stabilisation method.
- eps
Eigenvalue floor.
Value
A list containing SAVE kernel, eigenvalues, directions, scores, and slices.
Examples
X <- as.matrix(mtcars[, c("disp", "hp", "wt")])
fit <- compute_save(X, y = mtcars$mpg, nslices = 4)
head(fit$eigenvalues)
#> [1] 0.9789691 0.8509684 0.5671544