ValidationTest: Prognostic index (PI) and Kaplan-Meier curves on testing set
In cosmonet-package/COSMONET: Screening-network Cox methods for survival analysis

This function compute the prognostic index PI^{D} on the testing set D using the regression coefficients and the optimal cutoff computed on the training set T. Then, the kaplan-Meier curves are shown as resulting of the log-rank test between high- and low-risk group.

1	ValidationTest(x, y, beta, opt.cutoff)

`x`	input testing 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 cofficients estimated on the training set `T`.
`opt.cutoff`	optimal cutoff selected adaptively on the training set `T`.

The following objects are returned:

`df`	data frame about sample, prognostic index, time, status and group risk on testing set `D`.
`p.value`	`p`-value resulting from the log-rank test (the significance level is `p`-value < 0.05).
`plots`	survival curves and distribution plot of prognostic index `PI^{D}`.