rglaft {afthd} | R Documentation |
Provides Estimates of selected variable in parametric AFT model with smooth time functions for univariate in high dimensional gene expression data without MCMC.Incorporated variable selection has been done with regularization technique. It also deals covariates with missing values.
@details Survival time T for covariate x, is modelled as AFT model using
S(T|x)=S_0(T\exp(-\eta(x;\beta)))
and baseline survival function is modelled as
S_0(T)=\exp(-\exp(\eta_0(log(T);\beta_0)))
Where \eta
and \eta
are linear predictor.
rglaft(m, n, STime, Event, alpha, data)
m |
Starting column number of covariates of study from high dimensional entered data. |
n |
Ending column number of covariates of study from high dimensional entered data. |
STime |
name of survival time in data. |
Event |
name of event in data. 0 is for censored and 1 for occurrence of event. |
alpha |
It is chosen value between 0 and 1 to know the regularization method. alpha=1 for Lasso, alpha=0 for Ridge and alpha between 0 and 1 for elastic net regularization. |
data |
High dimensional gene expression data that contains event status, survival time and and set of covariates. |
Matrix that contains survival information of selected covariates(selected from chosen columns using regularization) on AFT model. Uppermost covariates are more significant than lowerone, as covariates are ordered as their increasing order of p value.
Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari
pvaft, rglwbysu, rglwbysm
##
data(hdata)
set.seed(1000)
rglaft(9,50,STime="os",Event="death",1,hdata)
##