cv.ELMCoxBAR: Cross-Validation for ELMCoxBAR

In ELMSurv: Extreme Learning Machine for Survival Analysis

Description

Cross-Validation for ELMCoxBAR to tune kernel parameters Based on log Likelihood

Usage

cv.ELMCoxBAR(x, y, Kernel_type = "lin_kernel", Kernel_para = c(2, 1),
  penality = 0, maxiter = 5, nfolds = 2, ...)

Arguments

x	The covariates(predictor variables) of training data.
y	Survival time and censored status of training data. Must be a Surv survival object
Kernel_type	Type of kernel matrix. Currently four options avaibable. "RBF_kernel",a RBF kernel;"lin_kernel" , a linear kernel;poly_kernel ,a polynomial kernel;sigmoid_kernel, a sigmoid kernel. Default is "lin_kernel".
Kernel_para	Parameters for different types of kernels. A single value for RBF and linear kernels. A vector for polynomial and sigmoid kernels and progam stops if only a single value is supplied. However, if the vector of values is supplied in the cases of RBF and liner kernels, only the first value will be used. Default is a vector value "c(2,1)".
penality	Currently, penality is defaulted to 0 to train an ELMCoxBAR model.
maxiter	Maximum values of iterations to update the CoxBAR estimator. Default is 5.
nfolds	Number of folds in cross validation.
...	Additional arguments for glmnet.

Value

Object of class ELMCoxBAR with elements

elmcox	A glmnet type model. See glmnet for details.
trainx	Training data covariates.
kerneltype	Type of kernel matrix used in training. kerneltype=1,a RBF kernel;kerneltype=2 , a linear kernel;kerneltype=3 ,a polynomial kernel;kerneltype=4, a sigmoid kernel.
Kernel_para	Optimal Parameters returned by cross validation. A single value for kerneltype=1 or 2. A vector for kerneltype=3 or 4.

Author(s)

Hong Wang

References

• Wang, H, Li, G. Extreme learning machine Cox model for high-dimensional survival analysis. Statistics in Medicine. 2019; 38:2139-2156.

Examples

set.seed(123)
require(ELMSurv)
require(survival)
#Lung DATA
data(lung)
lung=na.omit(lung)
lung[,3]=lung[,3]-1
n=dim(lung)[1]
L=sample(1:n,ceiling(n*0.5))
trset<-lung[L,]
teset<-lung[-L,]
rii=c(2,3)
# Default with lin_kernel 
elmsurvmodel=cv.ELMCoxBAR(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
#The predicted linear predictor
bestpara=elmsurvmodel$Kernel_para

ELMSurv documentation built on May 27, 2019, 9:04 a.m.