ridgeSIR | R Documentation |
ridgeSIR
performs the first step of the method (ridge regularization
of SIR)
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, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr
Picheny, V., Servien, R. and Villa-Vialaneix, N. (2019) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29(2), 255–267.
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)
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