sparseSIR: sparse SIR

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

View source: R/sparseSIR.R

Description

sparseSIR performs the second step of the method (shrinkage of ridge SIR results

Usage

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sparseSIR(object, inter_len, adaptive = FALSE, sel_prop = 0.05,
  parallel = FALSE, ncores = NULL)

Arguments

object

an object of class ridgeRes as obtained from the function ridgeSIR

inter_len

(numeric) vector with interval lengths

adaptive

should the function returns the list of strong zeros and non strong zeros (logical). Default to FALSE

sel_prop

used only when adaptive = TRUE. Fraction of the coefficients that will be considered as strong zeros and strong non zeros. Default to 0.05

parallel

whether the computation should be performed in parallel or not. Logical. Default is FALSE

ncores

number of cores to use if parallel = TRUE. If left to NULL, all available cores minus one are used

Value

S3 object of class sparseRes: a list consisting of

@details Different quality criteria used to select the best models among a list of models with different interval definitions. Quality criteria are: log-likelihood (loglik), cross-validation error as provided by the function glmnet, two versions of the AIC (AIC and AIC2) and of the BIC (BIC and BIC2) in which the number of parameters is either the number of non null intervals or the number of non null parameters with respect to the original variables.

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.

See Also

ridgeSIR, project.sparseRes, SISIR

Examples

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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, mu2 = 10^8)
res_sparse <- sparseSIR(res_ridge, rep(10, 20))

SISIR documentation built on May 29, 2017, 8:31 p.m.