elm_surv: ELMSurv elm_surv

In whcsu/ELMSurv: Extreme Learning Machine for Survival Analysis

Description

Extreme Learning Machine Using the Buckley-James estimator

Usage

elm_surv(trainx, trainy, testx, Regularization_coefficient, kerneltype,
  Kernel_para)

Arguments

trainx	The covariates(predictor variables) of training data.
trainy	Survival time and censored status of training data. Must be a Surv survival object
testx	The covariates(predictor variables) of test data.
Regularization_coefficient	Ridge or Tikhonov regularization parameter. Default value for ELMSurvEN is 10000. It need be set by the user here when using a single base ELM survival model. Also known as C in the ELM paper.
kerneltype	Type of kernel matrix. kerneltype=1,a RBF kernel;kerneltype=2 , a linear kernel;kerneltype=3 ,a polynomial kernel;kerneltype=4, a sigmoid kernel.
Kernel_para	Parameters for different types of kernels. A single value for kerneltype=1 or 2. A vector for kerneltype=3 or 4.

Value

List of returned values

elmsurvfit	Mean Square Error(MSE) on training data.
newy	Esitmated survival times of training data by the Buckley-James estimator.
outputWeight	Weights of the output layer in ELM.
testpre	The estimated survival times for testx data.

Author(s)

Hong Wang

References

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

See Also

ELMSurvEN

whcsu/ELMSurv documentation built on May 6, 2019, 5:04 p.m.