hMave: Hazard Mave for Censored Survival Data

In orthoDr: Semi-Parametric Dimension Reduction Models Using Orthogonality Constrained Optimization

Hazard Mave for Censored Survival Data

Description

This is an almost direct R translation of Xia, Zhang & Xu's (2010) hMave MATLAB code. We implemented further options for setting a different initial value. The computational algorithm does not utilize the orthogonality constrained optimization.

Usage

hMave(x, y, censor, m0, B0 = NULL)

Arguments

x	A matrix for features.
y	A vector of observed time.
censor	A vector of censoring indicator.
m0	number of dimensions to use
B0	initial value of B. This is a feature we implemented.

Value

A list consisting of

B	The estimated B matrix
cv	Leave one out cross-validation error

References

Xia, Y., Zhang, D., & Xu, J. (2010). Dimension reduction and semiparametric estimation of survival models. Journal of the American Statistical Association, 105(489), 278-290. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/jasa.2009.tm09372")}

Examples

# generate some survival data
set.seed(1)
P <- 7
N <- 150
dataX <- matrix(runif(N * P), N, P)
failEDR <- as.matrix(cbind(c(1, 1.3, -1.3, 1, -0.5, 0.5, -0.5, rep(0, P - 7))))
T <- exp(dataX %*% failEDR + rnorm(N))
C <- runif(N, 0, 15)
Y <- pmin(T, C)
Censor <- (T < C)

# fit the model
hMave.fit <- hMave(dataX, Y, Censor, 1)

orthoDr documentation built on April 30, 2023, 5:12 p.m.