ridgeSIR: ridge SIR
In SISIR: Sparse Interval Sliced Inverse Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

ridgeSIR performs the first step of the method (ridge regularization of SIR)

1	ridgeSIR(x, y, H, d, mu2 = NULL)

`x`	explanatory variables (numeric matrix or data frame)
`y`	target variable (numeric vector)
`H`	number of slices (integer)
`d`	number of dimensions to be kept
`mu2`	ridge regularization parameter (numeric, positive)

SI-SIR

S3 object of class ridgeRes: a list consisting of

EDR the estimated EDR space (a p x d matrix)
condC the estimated slice projection on EDR (a d x H matrix)
eigenvalues the eigenvalues obtained during the generalized eigendecomposition performed by SIR
parameters a list of hyper-parameters for the method:
- H number of slices
- d dimension of the EDR space
- mu2 regularization parameter for the ridge penalty
utils useful outputs for further computations:
- Sigma covariance matrix for x
- slices slice number for all observations
- invsqrtS value of the inverse square root of the regularized covariance matrix for x

Victor Picheny, [email protected]

Remi Servien, [email protected]

Nathalie Villa-Vialaneix, [email protected]

Picheny, V., Servien, R. and Villa-Vialaneix, N. (2016) Interpretable sparse SIR for digitized functional data. Preprint.

sparseSIR, SISIR, tune.ridgeSIR

set.seed(1140)
tsteps <- seq(0, 1, length = 50)
simulate_bm <- function() return(c(0, cumsum(rnorm(length(tsteps)-1, sd=1))))
x <- t(replicate(50, simulate_bm()))
beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2)) 
y <- log(abs(x %*% beta[ ,1])) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(50, sd = 0.1)
res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
## Not run: print(res_ridge)