Main and utility functions for training the Cox PH model.

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.

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).

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.

Examples

# DO NOT CALL THIS FUNCTION DIRECTLY. # Use SuperPCA_pVals() instead if (FALSE) { 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 ) }