ELMSurv (version 0.6)

ELMBJEN: ELMBJEN

Description

A Survival Ensemble of Extreme Learning Machine Using the Buckley-James estimator

Usage

ELMBJEN(x, y, mtry = floor(sqrt(ncol(x))), trlength = 100,
  Regularization_coefficient = 10000, 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

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)"

Value

Object of class ELMSurvEN with elements

elmsurvfit	A list of base models ELMBJ of size trlength. To retrieve a particular base model: use elmsurvfit[[i]], where i takes values between 1 and trlength
colindexes	Covaraite subspace index.
trlength	Number of bases models trained.

References

• Hong Wang et al (2018). A Survival Ensemble of Extreme Learning Machine. Applied Intelligence, DOI:10.1007/s10489-017-1063-4.

See Also

ELMBJ

Examples

# 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)
elmsurvmodel=ELMBJEN(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
# Get the 1th base model
firstbasemodel=elmsurvmodel$elmsurvfit[[1]]
# }