double.slice {censorSIR}R Documentation

double slice both censored data and un-censored data

Description

double slice both censored data and un-censored data.

Usage

double.slice(y, delta, x, n.slice1, n.slice0)

Arguments

y survival time
delta censor indicator, 0 means censored, 1 means not
x data matrix of predictors, each column in one predictor
n.slice0 number of slices for censored data
n.slice1 number of slices for uncensored data

Details

Value

values the eigen values of eigen decomposition
vectors the eigen vectors of eigen decomposition

Note

need to run cen.sir to further find dimension reduction directions of lifetime

Author(s)

Wei Sun sunwei@stat.ucla.edu

References

Ker-chau Li, Jane-ling Wang and Chun-Hou Chen (1999) Dimension reduction for censored regression data. The Annals of Statistics 27, 1-23.

See Also

cen.sir

Examples

# --- set up parameters ---
n.slice  = 10;
n.slice1 = 5;
n.slice0 = 5;
h = 0.2;
c = 0.05;
N = 300;

# --- generate data ---
id = c(1:N);
x1 = rnorm(N,0,1); x2 = rnorm(N,0,1); x3 = rnorm(N,0,1);
x4 = rnorm(N,0,1); x5 = rnorm(N,0,1); x6 = rnorm(N,0,1);
x = cbind(x1,x2,x3,x4,x5,x6);
err1 = rnorm(N,0,0.1);
err2 = runif(N,0,0.1);
# survival time is only related with x1
Y0 = 4 - abs(x1-1) + err1; 
# censor time is related with x1-x3
C  = 3 + err2;
C[x1<=0 | x2+x3<=0] = 10 
y  = pmin(Y0,C);
delta= y; delta[(Y0 <= C)] = 1; delta[(Y0 > C)] = 0;

# --- double-slicing ---
ds = double.slice(y, delta, x, n.slice1, n.slice0);
ds
plot.3d.sir(ds)

joint.edrs  = edr.n(ds, 2);

# --- find sir direction ---
sir = cen.sir(y, delta, x, n.slice, joint.edrs, h, c)
sir


[Package censorSIR version 1.0 Index]