CSIS | R Documentation |
A model-free and data-adaptive feature screening method for ultrahigh-dimensional data and even survival data. The proposed method is based on the concordance index which measures concordance between random vectors even if one of the vectors is a survival object Surv. This rank correlation based method does not require specifying a regression model, and applies robustly to data in the presence of censoring and heavy tails. It enjoys both sure screening and rank consistency properties under weak assumptions.
CSIS(X, Y, nsis = (dim(X)[1])/log(dim(X)[1]))
X |
The design matrix of dimensions n * p. Each row is an observation vector. |
Y |
The response vector of dimension n * 1. For survival models, Y should be an object of class Surv, as provided by the function Surv() in the package survival. |
nsis |
Number of predictors recruited by CSIS. The default is n/log(n). |
the labels of first nsis largest active set of all predictors
Xuewei Cheng xwcheng@hunnu.edu.cn
Cheng X, Li G, Wang H. The concordance filter: an adaptive model-free feature screening procedure[J]. Computational Statistics, 2023: 1-24.
## Scenario 1 generate complete data
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)]
A1 <- CSIS(X, Y, n / log(n))
A1
## Scenario 2 generate survival data
library(survival)
n <- 100
p <- 200
rho <- 0.5
data <- GendataCox(n, p, rho)
data <- cbind(data[[1]], data[[2]], data[[3]])
colnames(data)[ncol(data)] <- c("status")
colnames(data)[(ncol(data) - 1)] <- c("time")
colnames(data)[(1:(ncol(data) - 2))] <- c(paste0("X", 1:(ncol(data) - 2)))
data <- as.matrix(data)
X <- data[, 1:(ncol(data) - 2)]
Y <- Surv(data[, (ncol(data) - 1)], data[, ncol(data)])
A2 <- CSIS(X, Y, n / log(n))
A2
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