rk_subsample {kko}

compute selection frequency of rk_fit on subsamples

Description

The function applys rk_fit on subsamples and record selection results.

Usage

rk_subsample(
  X,
  y,
  X_k,
  rfn,
  n_stb,
  cv_folds,
  frac_stb = 1/2,
  nCores_para,
  rkernel = "laplacian",
  rk_scale = 1
)

Arguments

X

design matrix of additive model; rows are observations and columns are variables.

y

response of addtive model.

X_k

knockoffs matrix of design; the same size as X.

rfn

random feature expansion number.

n_stb

number of subsampling.

cv_folds

the folds of cross-validation for tuning group lasso.

frac_stb

fraction of subsample size.

nCores_para

number of cores for parallelizing subsampling.

rkernel

kernel choices. Default is "laplacian". Other choices are "cauchy" and "gaussian".

rk_scale

scaling parameter of sampling distribution for random feature expansion. For gaussian kernel, it is standard deviation of gaussian sampling distribution.

Value

a 0/1 matrix indicating selection results. Rows are subsamples, and columns are variables. The first half columns are variables of design X, and the latter are knockoffs X_k.

Author(s)

Xiaowu Dai, Xiang Lyu, Lexin Li

Examples

library(knockoff)
p=5 # number of predictors
sig_mag=100 # signal strength
n= 100 # sample size
rkernel="laplacian" # kernel choice
s=2  # sparsity, number of nonzero component functions
rk_scale=1  # scaling paramtere of kernel
rfn= 3  # number of random features
cv_folds=15  # folds of cross-validation in group lasso
n_stb=10 # number of subsampling
frac_stb=1/2 # fraction of subsample
nCores_para=2 # number of cores for parallelization
X=matrix(rnorm(n*p),n,p)%*%chol(toeplitz(0.3^(0:(p-1))))   # generate design
X_k = create.second_order(X) # generate knockoff
reg_coef=c(rep(1,s),rep(0,p-s))  # regression coefficient
reg_coef=reg_coef*(2*(rnorm(p)>0)-1)*sig_mag
y=X%*% reg_coef + rnorm(n) # response

rk_subsample(X,y,X_k,rfn,n_stb,cv_folds,frac_stb,nCores_para,rkernel,rk_scale)