hybridFit: survivalsvm (hybrid approach)
In survivalsvm: Survival Support Vector Analysis

Description Usage Arguments Value Author(s)

fits survivalsvm model based on hybrid approach method for survival support vector ananlysis.

hybridFit(X, Y, delta, meth_par = c(1, 1), kernel_type = "lin_kernel",
  kernel_pars = NA, bin_cat = integer(0), makediff = makediff3,
  opt_alg = "quadprog", sgf_sv = 5, sigf = 7, maxiter = 40,
  margin = 0.05, bound = 10, eig.tol = 1e-06, conv.tol = 1e-07,
  posd.tol = 1e-08)

`X`	[`matrix(1)`] Matrix of training data points.
`Y`	[`vector(1)`] Vector of survival times.
`delta`	[`vector(1)`] Vector of status: 1 = not censored.
`meth_par`	[`numeric(1)`] Parameters of regularization.
`kernel_type`	[`character(1)`] Kernel that is used to fit the model. The handled type are: linear kern ('lin_kern'), additive kernel ('add_kernel'), radial basis kernels ('rbf_kernel' and 'rbf4_kernel') and the polynomial kernel ('poly_kernel').
`kernel_pars`	[`numeric(1)\|vector(1)`] Parameters of kernel, when required.
`bin_cat`	[`vector(1)`] Indexes of binary/categorical varibales
`makediff`	[`character(1)`] String indicating which of `'makediff1'`, `'makediff2'` or `'makediff3'` will be used.
`opt_alg`	[`character(1)`] Program that will be used to solve the quadratic optimization problem. Either `quadprog` or `ipop`.
`sgf_sv`	[`integer(1)`] Number of decimal digits in the solution of the quadratic optimization problem.
`sigf`	[`integer(1)`] Used by `ipop`. See `ipop` for details.
`maxiter`	[`integer(1)`] Used by `ipop`. See `ipop` for details.
`margin`	[`numeric(1)`] Used by `ipop`. See `ipop` for details.
`bound`	[`numeric(1)`] Used by `ipop`. See `ipop` for details.
`eig.tol`	[`numeric(1)`] Used by `nearPD` for adjusting positive definiteness. See `nearPD` for detail.
`conv.tol`	[`numeric(1)`] Used by `nearPD` for adjusting positive definiteness. See `nearPD` for detail.
`posd.tol`	[`numeric(1)`] Used by `nearPD` for adjusting positive definiteness. See `nearPD` for detail.

[Hybrid(1)] Object of class Hybrid containing elements:

`Alpha`	Solution of the quadratic optimization problem,
`Xtrain`	Matrix of training points,
`DifMat`	Matrix used to maked differences between neighbor points,
`Kernel`	Kernel matrix, an object of class `Kernel`,
`OptMeth`	Program used to solve the quadratic optimization problem.