# project: sparse SIR In SISIR: Sparse Interval Sliced Inverse Regression

## Description

`project` performs the projection on the sparse EDR space (as obtained by the `glmnet`)

## Usage

 ```1 2 3 4``` ```## 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, [email protected]

Remi Servien, [email protected]

Nathalie Villa-Vialaneix, [email protected]

## References

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

`sparseSIR`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```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) ```