CosmonetTraining: Fit network-regularized Cox regression models on training set
In cosmonet-package/COSMONET: Screening-network Cox methods for survival analysis

This function fits penalized Cox regression methods in order to incorporate gene regulatory relationships and to select signature genes using the training set T.

CosmonetTraining(
  k,
  x,
  y,
  screenVars,
  family = "Cox",
  penalty = "Net",
  Omega,
  alpha = 0.5,
  lambda = NULL,
  nlambda = 50,
  nfolds = 5,
  foldid = NULL,
  selOptLambda = min("min", "1se"),
  optCutpoint = c("minPValue", "median", "survCutpoint")
)

`k`	times to loop through cross validation.
`x`	input training matrix `nxp`. Each row is an observation vector.
`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.
`screenVars`	screened variables obtained from BMD- or DAD-, or BMD+DAD-screening or by the user. A list or a character string can be used.
`family`	Cox proportional hazards regression model. `Family=Cox`
`penalty`	penalty type. Can choose `Net` where `Omega` matrix is requested. For `penalty = Net`, the penalty is defined as λ{α\|\|β\|\|_1+(1-\lapha)/2(β^{T}Lβ)}*, where `L` is a Laplacian matrix calculated from `Omega`.
`Omega`	adjacency matrix with zero diagonal and non-negative off-diagonal used to calculate Laplacian matrix.
`alpha`	ratio between `L_1` and Laplacian for `Net`. Default is `alpha = 0.5`.
`lambda`	a user supplied decreasing sequence. If `lambda = NULL`, a sequence of lambda is generated based on `nlambda` and `rlambda` (for more details, see `APML0` package). Supplying a value of lambda overrides this.
`nlambda`	number of lambda values. Default is 50.
`nfolds`	number of folds performed for tuning optimal parameters over runs. Default is `nfolds = 5`.
`foldid`	an optional vector of values between 1 and nfolds specifying which fold each observation is in.
`selOptLambda`	a character string for selecting the lambda parameter. Options are `min` which uses the regularisation procedure implemented in the APML0 package or `1se"` to select the lambda parameter within one standard error from the optimal value.
`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`.

The following objects are returned:

`beta`	a sparse Matrix of coefficients, stored in class`dgCMatrix`.
`opt.lambdas`	lambda values based on minimum `cvm` over runs and on `1se` to select the lambda parameter within one standard error from the optimal value.
`df`	data frame composed by samples, relative prognostic indices, times, status and group risk for each quantile q_{gamma}, with γ=0.20, ..., 0.80.
`summary`	summary table on number of patients at risk, cutoff and p.value for each quantile.
`opt.cutoff`	optimal cutoff selected on the training.
`p.value`	resulting from the log-rank test (the significance level is `p`-value < 0.05).