PSIS | R Documentation |
A novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh-dimensional predictor. This procedure is directly applicable to the situation with many classes.
PSIS(X, Y, nsis)
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 PSIS. The default is n/log(n). |
the labels of first nsis largest active set of all predictors
Xuewei Cheng xwcheng@hunnu.edu.cn
Pan, R., Wang, H., and Li, R. (2016). Ultrahigh-dimensional multiclass linear discriminant analysis by pairwise sure independence screening. Journal of the American Statistical Association, 111(513):169–179.
n <- 100
p <- 200
rho <- 0.5
data <- GendataLGM(n, p, rho)
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 <- PSIS(X, Y, n / log(n))
A
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