predict_Surv: Predict SurvMKL

In cwilso6/RMKL: Multiple Kernel Learning for Classification, Regression Problems, and Survival Settings

Description

This function is used to predict SpicyMKL models

Usage

predict_Surv(alpha, k0)

Arguments

alpha	coefficient
k0	the kernel cube needs prediction

Value

The predicted score

Examples

## Not run: 
library(RMKL)
library(kernlab)
data(Surv_data)
#Getting survival times in ascending order
ordtr <- order(Surv_data$time)
Surv_data_ordered = Surv_data[ordtr,]

xx = Surv_data_ordered[,1:2]
del = Surv_data_ordered$status
yy = Surv_data_ordered$time
if (!del[1]) {
  first1 <- which(del)[1]
  xx <- xx[-(1:(first1 - 1)), ]
  yy <- yy[-(1:(first1 - 1))]
  del <- del[-(1:(first1 - 1))]
  nn <- dim(Surv_data)[1] - first1 + 1
} else {
  nn <- dim(Surv_data)[1]
}

rho0 <- .001*(Surv_data$status - seq(0, 10, length.out = dim(Surv_data)[1]))
klist <- list(kernelMatrix(rbfdot(1), as.matrix(xx)),
              kernelMatrix(vanilladot(), as.matrix(xx)))
ktlist <- list(kernelMatrix(rbfdot(1), as.matrix(xx), as.matrix(Surv_data[,1:2])),
               kernelMatrix(vanilladot(), as.matrix(xx), as.matrix(Surv_data[,1:2])))
kk <- simplify2array(klist)
kkk <- simplify2array(ktlist)
modmkl <- SurvMKL(y = Surv_data$time, del = Surv_data$status, K = kk, rho =  rho0, 
C = 0.005, lambda = 0.5, maxiter = 500, cri = .01)
mkl = predict_Surv(modmkl, kkk)

## End(Not run)

cwilso6/RMKL documentation built on May 18, 2021, 9:58 a.m.