coxTrain_fun: Train Cox Proportional Hazards model for supervised PCA

In gabrielodom/pathwayPCA: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

Train Cox Proportional Hazards model for supervised PCA

Description

Main and utility functions for training the Cox PH model.

Usage

coxTrain_fun(x, y, censoring.status, s0.perc = NULL)

Arguments

x	A "tall" pathway data frame (p \times n).
y	A response vector of follow-up / event times.
censoring.status	A censoring vector.
s0.perc	A stabilization parameter. This is an optional argument to each of the functions called internally. Defaults to NULL.

Details

See https://web.stanford.edu/~hastie/Papers/spca_JASA.pdf, Section 5, for a description of Supervised PCA applied to survival data. The internal utility functions defined in this file (.coxscor, .coxvar, and .coxstuff) are not called anywhere else, other than in the coxTrain_fun function itself. Therefore, we do not document these functions.

NOTE: No missing values allowed.

Value

A list containing:

• tt : The scaled p-dimensional score vector: each value has been divided by the respective standard deviation plus the fudge value.

• numer : The original p-dimensional score vector. From the internal .coxscor function.

• sd : The standard deviations of the scores. From the internal .coxvar function.

• fudge : A regularization scalar added to the standard deviation. If s0.perc is supplied, fudge = quantile(sd, s0.perc).

Examples

  # DO NOT CALL THIS FUNCTION DIRECTLY.
  # Use SuperPCA_pVals() instead
  
## Not run: 
  p <- 500
  n <- 50

  x_mat <- matrix(rnorm(n * p), nrow = p, ncol = n)
  x_df <- data.frame(x_mat)
  time_int <- rpois(n, lambda = 365 * 2)
  obs_logi <- sample(
    c(FALSE, TRUE),
    size = n,
    replace = TRUE,
    prob = c(0.2, 0.8)
  )

  coxTrain_fun(
    x = x_df,
    y = time_int,
    censoring.status = !obs_logi
  )

## End(Not run)
  

gabrielodom/pathwayPCA documentation built on July 10, 2023, 3:32 a.m.