ELMCoxBoost: SurvELM ELMCoxBoost

In whcsu/SurvELM: Survival Anlalysis with Extreme Learning Machines

Description

An Extreme Learning Machine Cox Model with Likelihood Based Boosting

Usage

ELMCoxBoost(x, y, Kernel_type = "lin_kernel", Kernel_para = c(2, 1), ...)

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)".
...	Additional arguments for CoxBoost.

Value

Object of class ELMCoxBoost with elements

elmcoxboost	A CoxBoost model. See CoxBoost 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

See Also

CoxBoost

Examples

set.seed(123)
library(SurvELM)
library(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)
elmsurvmodel=ELMCoxBoost(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
#THE predicted linear predictor
testpre=predict(elmsurvmodel,teset[,-c(rii)])
#The predicted cumulative incidence function
testprecif=predict(elmsurvmodel,teset[,-c(rii)],type="CIF")
# The predicted partial log-likelihood
testprellk=predict(elmsurvmodel,teset[,-c(rii)],newtime=teset[,rii[1]],
newstatus=teset[,rii[2]],type="logplik")
uniquetimes=sort(unique(trset$time))
# The predicted probability of not yet having had the event at the time points given in times
testprerisk=predict(elmsurvmodel,teset[,-c(rii)],times=uniquetimes,type="risk")

whcsu/SurvELM documentation built on Jan. 28, 2020, 3:07 p.m.