project: sparse SIR

In SISIR: Select Intervals Suited for Functional Regression

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 sparseRes as obtained from the function sparseSIR

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, [e-mail]
Remi Servien, [e-mail]
Nathalie Vialaneix, [e-mail]

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

sparseSIR

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)

res_ridge <- ridgeSIR(x, y, H = 10, d = 2)
res_sparse <- sparseSIR(res_ridge, rep(1, ncol(x)))
proj_data <- project(res_sparse)

SISIR documentation built on Sept. 11, 2024, 7:40 p.m.