orthoDr_surv: Counting Process based semiparametric dimension reduction...

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

Counting Process based semiparametric dimension reduction (IR-CP) model

Description

Models the data according to the counting process based semiparametric dimension reduction (IR-CP) model for right censored survival outcome.

Usage

orthoDr_surv(
  x,
  y,
  censor,
  method = "dm",
  ndr = ifelse(method == "forward", 1, 2),
  B.initial = NULL,
  bw = NULL,
  keep.data = FALSE,
  control = list(),
  maxitr = 500,
  verbose = FALSE,
  ncore = 0
)

Arguments

x	A matrix or data.frame for features. The algorithm will not scale the columns to unit variance
y	A vector of observed time
censor	A vector of censoring indicator
method	Estimation equation to use. Either: "forward" (1-d model), "dn" (counting process), or "dm" (martingale).
ndr	The number of directions
B.initial	Initial B values. Will use the counting process based SIR model CP_SIR as the initial if leaving as NULL. If specified, must be a matrix with ncol(x) rows and ndr columns. Will be processed by Gram-Schmidt if not orthogonal.
bw	A Kernel bandwidth, assuming each variables have unit variance.
keep.data	Should the original data be kept for prediction. Default is FALSE
control	A list of tuning variables for optimization. epsilon is the size for numerically approximating the gradient. For others, see Wen and Yin (2013).
maxitr	Maximum number of iterations
verbose	Should information be displayed
ncore	Number of cores for parallel computing. The default is the maximum number of threads.

Value

A orthoDr object consisting of list with named elements:

B	The optimal B value
fn	The final functional value
itr	The number of iterations
converge	convergence code

References

Sun, Q., Zhu, R., Wang, T., & Zeng, D. (2019). Counting process-based dimension reduction methods for censored outcomes. Biometrika, 106(1), 181-196. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/asy064")}

Examples

# This is setting 1 in Sun et. al. (2017) with reduced sample size
library(MASS)
set.seed(1)
N <- 200
P <- 6
V <- 0.5^abs(outer(1:P, 1:P, "-"))
dataX <- as.matrix(mvrnorm(N, mu = rep(0, P), Sigma = V))
failEDR <- as.matrix(c(1, 0.5, 0, 0, 0, rep(0, P - 5)))
censorEDR <- as.matrix(c(0, 0, 0, 1, 1, rep(0, P - 5)))
T <- rexp(N, exp(dataX %*% failEDR))
C <- rexp(N, exp(dataX %*% censorEDR - 1))
ndr <- 1
Y <- pmin(T, C)
Censor <- (T < C)

# fit the model
forward.fit <- orthoDr_surv(dataX, Y, Censor, method = "forward")
distance(failEDR, forward.fit$B, "dist")

dn.fit <- orthoDr_surv(dataX, Y, Censor, method = "dn", ndr = ndr)
distance(failEDR, dn.fit$B, "dist")

dm.fit <- orthoDr_surv(dataX, Y, Censor, method = "dm", ndr = ndr)
distance(failEDR, dm.fit$B, "dist")

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