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

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

Grow a rotation survival random forest

1 2	rrotsfpca(x, y, trlength = 500, m = 2, mtry = floor(sqrt(ncol(x))), control = control, na.action = na.omit, vari_status = FALSE)

`x`	The covariates(predictor variables) of training data.
`y`	Survival time and censored status of training data. Must be a Surv `survival` object
`trlength`	The ensemle size (the number of base ELM survival models). Default is 100.
`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.
`testx`	The covariates(predictor variables) of test data.
`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

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

elm_surv

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]]