HybridObj

[<code>vector(1)</code>]
A part of the solution of the quadratic optimization problem of interest.

Alpha

Beta

Betastar

[<code>vector(1)</code>]
Vector of status 1 = no censored.

Delta

[<code>matrix(1)</code>]
Matrix of training data points.

Xtrain

[<code><a rd-options="" href="/link/Diffmatrix?package=survivalsvm&version=0.0.5" data-mini-rdoc="survivalsvm::Diffmatrix">Diffmatrix</a>(1)</code>]
Matrix used to maked differences between neighbor points.

DifMat

[<code><a rd-options="" href="/link/Kernel?package=survivalsvm&version=0.0.5" data-mini-rdoc="survivalsvm::Kernel">Kernel</a>(1)</code>]
Object of class <code>Kernel</code>.

Kernel

[<code><a rd-options="" href="/link/Kernel?package=survivalsvm&version=0.0.5" data-mini-rdoc="survivalsvm::Kernel">Kernel</a>(1)</code>]
Program used to solve the optimization problem.

OptMeth

[<code>numeric(1)</code>]
The estimated offset.

Constructs object of class <code>VB2FitObj</code>.

internal

Performs support vectors analysis for data sets with survival
outcome. Three approaches are available in the package: The regression approach
takes censoring into account when formulating the inequality constraints of
the support vector problem. In the ranking approach, the inequality constraints
set the objective to maximize the concordance index for comparable pairs
of observations. The hybrid approach combines the regression and ranking
constraints in the same model.

Cesaire Fouodo

survivalsvm

Survival Support Vector Analysis

HybridObj function

<dl><dt>Alpha</dt>
<dd>[<code>vector(1)</code>] 
A part of the solution of the quadratic optimization problem of interest.</dd>
<dt>Beta</dt>
<dd>[<code>vector(1)</code>] 
A part of the solution of the quadratic optimization problem of interest.</dd>
<dt>Betastar</dt>
<dd>[<code>vector(1)</code>] 
A part of the solution of the quadratic optimization problem of interest.</dd>
<dt>Delta</dt>
<dd>[<code>vector(1)</code>] 
Vector of status 1 = no censored.</dd>
<dt>Xtrain</dt>
<dd>[<code>matrix(1)</code>] 
Matrix of training data points.</dd>
<dt>DifMat</dt>
<dd>[<code>Diffmatrix(1)</code>] 
Matrix used to maked differences between neighbor points.</dd>
<dt>Kernel</dt>
<dd>[<code>Kernel(1)</code>] 
Object of class <code>Kernel</code>.</dd>
<dt>OptMeth</dt>
<dd>[<code>Kernel(1)</code>] 
Program used to solve the optimization problem.</dd>
<dt>b0</dt>
<dd>[<code>numeric(1)</code>] 
The estimated offset.</dd></dl>

Arguments

Constructs object of class <code>VB2FitObj</code>.

HybridObj (hybrid approach) — HybridObj

<dl>

<dt>Alpha</dt>
<dd>[<code>vector(1)</code>] 
A part of the solution of the quadratic optimization problem of interest.</dd>


<dt>Beta</dt>
<dd>[<code>vector(1)</code>] 
A part of the solution of the quadratic optimization problem of interest.</dd>


<dt>Betastar</dt>
<dd>[<code>vector(1)</code>] 
A part of the solution of the quadratic optimization problem of interest.</dd>


<dt>Delta</dt>
<dd>[<code>vector(1)</code>] 
Vector of status 1 = no censored.</dd>


<dt>Xtrain</dt>
<dd>[<code>matrix(1)</code>] 
Matrix of training data points.</dd>


<dt>DifMat</dt>
<dd>[<code>Diffmatrix(1)</code>] 
Matrix used to maked differences between neighbor points.</dd>


<dt>Kernel</dt>
<dd>[<code>Kernel(1)</code>] 
Object of class <code>Kernel</code>.</dd>


<dt>OptMeth</dt>
<dd>[<code>Kernel(1)</code>] 
Program used to solve the optimization problem.</dd>


<dt>b0</dt>
<dd>[<code>numeric(1)</code>] 
The estimated offset.</dd>

</dl>

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

HybridObj: `HybridObj` (hybrid approach)

Description

Usage

Value

Arguments