ELMCoxBAR: ELMCoxBAR

In ELMSurv: Extreme Learning Machine for Survival Analysis

Description

Extreme Learning Machine Cox Model for High Dimensional Survival Analysis

Usage

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

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.
...	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	Parameters used in training. 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=ELMCoxBAR(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
# with  the RBF kernel
elmsurvmodel=ELMCoxBAR(x=trset[,-rii],y=Surv(trset[,rii[1]], 
trset[,rii[2]]),Kernel_type="RBF_kernel",Kernel_para=c(2,1))
#The predicted linear predictor
testprelin=predict(elmsurvmodel,teset[,-c(rii)])

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