ELMSurvEN: ELMSurv ELMSurvEN
In whcsu/ELMSurv: Extreme Learning Machine for Survival Analysis

Description Usage Arguments Value Author(s) References See Also Examples

An Survival Ensemble of Extreme Learning Machine

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ELMSurvEN(x, y, testx, mtry = floor(sqrt(ncol(x))), trlength = 100,
  Regularization_coefficient = 10000, Kernel_type = "lin_kernel",
  Kernel_para = c(2, 1))

`x`	The covariates(predictor variables) of training data.
`y`	Survival time and censored status of training data. Must be a Surv `survival` object
`testx`	The covariates(predictor variables) of test data.
`mtry`	The number of covariates(predictor variables) used in each base ELM model. Default is the square root of the number of all avaibable covariates.
`trlength`	The ensemle size (the number of base ELM survival models). Default is 100.
`Regularization_coefficient`	Ridge or Tikhonov regularization parameter. Default is 10000. Also known as C in the ELM paper.
`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)"

Object of class ELMSurvEN with elements

`elmsurvfit`	A list of base models `elm_surv` of size `trlength`. To retrieve a particular base model: use elmsurvfit[[i]], where i takes values between 1 and `trlength`
`precitedtime`	Esitmated survival times of test data.

Hong Wang

Hong Wang et al (2017). A Survival Ensemble of Extreme Learning Machine. Applied Intelligence, in press.

elm_surv

## Not run: 
set.seed(123)
require(ELMSurv)
require(survival)
## Survival Ensemble of ELM  with default settings
#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)
elmsurvfit=ELMSurvEN(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]),testx=teset[,-c(rii)])
# Get the 5th base model
basemodel=elmsurvfit[[1]]
#Print the c-index values
#library(survcomp)
#ci_elm=concordance.index(-rowMeans(elmsurvfit$precitedtime),teset$days,teset$status)[1] 
#print(ci_elm)

## End(Not run)