pvaft {afthd} | R Documentation |
Provides list of covariates and their estimates of parametric AFT model with smooth time functions, whose p value is less than chosen value (by default p=1 that is all chosen covariates come in result). Using AFT model for univariate in high dimensional data without MCMC.
pvaft(m, n, STime, Event, p = 1, data)
m |
Starting column number of covariates of study in high dimensional entered data. |
n |
Ending column number of covariates of study in 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. |
p |
p-value, to make restriction for selection of covariates, default value is 1. |
data |
High dimensional gene expression data that contains event status, survival time and and set of covariates. |
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.
Matrix that contains survival information of selected covariates(selected from chosen columns whose p value is <= p) on AFT model. Result shows together for all covariates chosen from column m to n.
Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari
wbysuni,wbysmv, rglaft
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data(hdata)
pvaft(9,30,STime="os",Event="death",0.1,hdata)
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