coxTrain_fun {pathwayPCA} | R Documentation

## 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)

[Package pathwayPCA version 1.2.0 Index]