| PCSIS {MFSIS} | R Documentation |
Model-Free Feature Screening Based on the Projection Correlation
Description
A model-free screening method is based on the projection correlation which measures the dependence between two random vectors. This projection correlation based method does not require specifying a regression model, and applies to data in the presence of heavy tails and multivariate responses. It enjoys both sure screening and rank consistency properties under weak assumptions.
Usage
PCSIS(X, Y, nsis = (dim(X)[1])/log(dim(X)[1]))
Arguments
X |
The design matrix of dimensions n * p. Each row is an observation vector. |
Y |
The response vector of dimension n * 1. |
nsis |
Number of predictors recruited by PCSIS. The default is n/log(n). |
Value
the labels of first nsis largest active set of all predictors
Author(s)
Xuewei Cheng xwcheng@hunnu.edu.cn
References
Zhu, L., K. Xu, R. Li, and W. Zhong (2017). Projection correlation between two random vectors. Biometrika 104(4), 829–843.
Liu, W., Y. Ke, J. Liu, and R. Li (2020). Model-free feature screening and FDR control with knockoff features. Journal of the American Statistical Association, 1–16.
Examples
# have_numpy=reticulate::py_module_available("numpy")
# if (have_numpy){
# req_py()
n=100;
p=200;
rho=0.5;
data=GendataLM(n,p,rho,error="gaussian")
data=cbind(data[[1]],data[[2]])
colnames(data)[1:ncol(data)]=c(paste0("X",1:(ncol(data)-1)),"Y")
data=as.matrix(data)
X=data[,1:(ncol(data)-1)];
Y=data[,ncol(data)];
# A=PCSIS(X,Y,n/log(n));A
# }else{
# print('You should have the Python testing environment!')
#}