project {SISIR} | R Documentation |
sparse SIR
Description
project
performs the projection on the sparse EDR space (as obtained
by the glmnet
)
Usage
## S3 method for class 'sparseRes'
project(object)
project(object)
Arguments
object |
an object of class |
Details
The projection is obtained by the function
predict.glmnet
.
Value
a matrix of dimension n x d with the projection of the observations on the d dimensions of the sparse EDR space
Author(s)
Victor Picheny, victor.picheny@inrae.fr
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr
References
Picheny, V., Servien, R. and Villa-Vialaneix, N. (2016) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29(2), 255–267.
See Also
Examples
set.seed(1140)
tsteps <- seq(0, 1, length = 200)
nsim <- 100
simulate_bm <- function() return(c(0, cumsum(rnorm(length(tsteps)-1, sd=1))))
x <- t(replicate(nsim, simulate_bm()))
beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2))
beta[((tsteps < 0.2) | (tsteps > 0.5)), 1] <- 0
beta[((tsteps < 0.6) | (tsteps > 0.75)), 2] <- 0
y <- log(abs(x %*% beta[ ,1]) + 1) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(nsim, sd = 0.1)
## Not run:
res_ridge <- ridgeSIR(x, y, H = 10, d = 2)
res_sparse <- sparseSIR(res_ridge, rep(1, ncol(x)))
proj_data <- project(res_sparse)
## End(Not run)
[Package SISIR version 0.2.2 Index]