lgstbyuni {afthd} | R Documentation |
Provides estimate of AFT model with log logistic distribution using MCMC for univariate in high dimensional gene expression data. It also deals covariates with missing values.
lgstbyuni(m, n, STime, Event, nc, ni, 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 |
nc |
number of chain used in model. |
ni |
number of iteration used in model. |
data |
High dimensional gene expression data that contains event status, survival time and and set of covariates. |
This function deals covariates (in data) with missing values. Missing value in any column (covariate) is replaced by mean of that particular covariate.
AFT model is log-linear regression model for survival time T_{1}
, T_{2}
,..,T_{n}
.
i.e.,
log(T_i)= x_i'\beta +\sigma\epsilon_i ;~\epsilon_i \sim F_\epsilon (.)~which~is~iid
Where F_\epsilon
is known cdf which is defined on real line.
When baseline distribution is logistic then T follows log logistic distribution.
T \sim Log-Logis(x'\beta,\sqrt{\tau)}
Data frame is containing posterior estimates (Coef, SD, Credible Interval, Rhat, n.eff) of regression coefficient of selected covariates and deviance. Result shows together for all covariates chosen from column m to n.
Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari
Prabhash et al(2016) <doi:10.21307/stattrans-2016-046>
wbysmv, lgnbymv, lgstbymvs
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data(hdata)
lgstbyuni(12,14,STime="os",Event="death",3,100,hdata)
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