SelectOptimalCutoff: Determine the optimal cutpoint and draw survival curves
In cosmonet-package/COSMONET: Screening-network Cox methods for survival analysis

Description Usage Arguments Details Value

This function computes the optimal cutoff PI^{*,T} on the training set T, i.e., the value that corresponds to the best separation in high-and-low risk group with respect to the log-rank test using the prognostic index PI^{T}.

SelectOptimalCutoff(
  x,
  y,
  beta,
  optCutpoint = c("minPValue", "median", "survCutpoint")
)

`x`	input training matrix `nxp`.
`y`	response variable, `y` should be a two-column data frame with columns named time and status. The latter is a binary variable, with 1 indicating event, and 0 indicating right censored. The rownames indicate the sample names ordered as the samples in the input testing matrix.
`beta`	regression coefficients estimated on training set `T`.
`optCutpoint`	a character string for choosing the optimal cutpoint on training set `T` based on prognostic index `PI^{T}`. Can choose `minPValue`, `median` and `survCutpoint`.

We use surv_cutpoint() to determine the optimal cutpoint and ggsurvplot() function to plot survival curves from survminer package.

The following objects are returned:

`df`	data frame about sample, prognostic index, time, status and group risk for each quantile q_{gamma}, with γ=0.25, ..., 0.80.
`summary`	summary information about number of patients at risk, cutoff and p.value for each quantile.
`opt.cutoff`	optimal cutoff selected.