tune.ridgeSIR: Cross-Validation for ridge SIR

In SISIR: Select Intervals Suited for Functional Regression

Cross-Validation for ridge SIR

Description

tune.ridgeSIR performs a Cross Validation for ridge SIR estimation

Usage

tune.ridgeSIR(
  x,
  y,
  listH,
  list_mu2,
  list_d,
  nfolds = 10,
  parallel = TRUE,
  ncores = NULL
)

Arguments

x	explanatory variables (numeric matrix or data frame)
y	target variable (numeric vector)
listH	list of the number of slices to be tested (numeric vector)
list_mu2	list of ridge regularization parameters to be tested (numeric vector)
list_d	list of the dimensions to be tested (numeric vector)
nfolds	number of folds for the cross validation. Default is 10
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

a data frame with tested parameters and corresponding CV error and estimation of R(d)

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

ridgeSIR

Examples

set.seed(1115)
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))
y <- log(abs(x %*% beta[ ,1])) + sqrt(abs(x %*% beta[ ,2]))
y <- y + rnorm(nsim, sd = 0.1)
list_mu2 <- 10^(0:10)
listH <- c(5, 10)
list_d <- 1:4
set.seed(1129)
## Not run: 
res_tune <- tune.ridgeSIR(x, y, listH, list_mu2, list_d, 
                          nfolds = 10, parallel = TRUE)
## End(Not run)

SISIR documentation built on March 31, 2023, 6:10 p.m.