rotsfpca: sfpls rostf_pca

In whcsu/rotsf: Survival Forest with PCA and Partial Least Squares

Description

Grow a rotation survival random forest

Usage

rotsfpca(formula, data, trlength = 500, m = 2, control = control,
  na.action = na.omit, vari_status = FALSE)

Arguments

trlength	The ensemle size (the number of base ELM survival models). Default is 100.
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.
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 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.

Author(s)

Hong Wang

References

• Zhou L, Xu Q, Wang H. (2015) Rotation survival forest for right censored data. PeerJ 3:e1009 https://doi.org/10.7717/peerj.1009.

See Also

elm_surv

Examples

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=ELMSurvEN(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]),testx=teset[,-c(rii)])
testpretimes=elmsurvmodel$precitedtime
#The predicted survival times on the first test example
head(testpretimes[1,])
#The predicted survival times of all test examples by the third model
head(testpretimes[,3])
# Get the 1th base model
firstbasemodel=elmsurvmodel$elmsurvfit[[1]]

whcsu/rotsf documentation built on Dec. 4, 2019, 2:10 p.m.