view_dr_surv: 2D or 2D view of survival data on reduced dimension

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

2D or 2D view of survival data on reduced dimension

Description

Produce 2D or 3D plots of right censored survival data based on a given dimension reduction space

Usage

view_dr_surv(
  x,
  y,
  censor,
  B = NULL,
  bw = NULL,
  FUN = "log",
  type = "2D",
  legend.add = TRUE,
  xlab = "Reduced Direction",
  ylab = "Time",
  zlab = "Survival"
)

Arguments

x	A matrix or data.frame for features (continuous only). The algorithm will not scale the columns to unit variance
y	A vector of observed time
censor	A vector of censoring indicator
B	The dimension reduction subspace, can only be 1 dimensional
bw	A Kernel bandwidth (3D plot only) for approximating the survival function, default is the Silverman's formula
FUN	A scaling function applied to the time points y. Default is "log".
type	⁠2D⁠ or ⁠3D⁠ plot
legend.add	Should legend be added (2D plot only)
xlab	x axis label
ylab	y axis label
zlab	z axis label

Value

An rgl object that is rendered.

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

# generate some survival data
N <- 100
P <- 4
dataX <- matrix(rnorm(N * P), N, P)
Y <- exp(-1 + dataX[, 1] + rnorm(N))
Censor <- rbinom(N, 1, 0.8)

orthoDr.fit <- orthoDr_surv(dataX, Y, Censor, ndr = 1, method = "dm")
view_dr_surv(dataX, Y, Censor, orthoDr.fit$B)

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