lgstbymv {afthd} | R Documentation |
Provides estimate of AFT model with log logistic distribution using MCMC for multivariable (maximum 5 covariates of column at a time) in high dimensional gene expression data. It also deals covariates with missing values.
lgstbymv(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. 0 is for censored and 1 for occurrence of event. |
nc |
number of MCMC chain. |
ni |
number of MCMC iteration to update the outcome. |
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 mean, sd, n.eff, Rhat and credible intervals (2.5%, 25%, 50%, 75% and 97.5%) for beta's, sigma, tau and deviance of the model for the selected covariates. beta[1] is for intercept and others are for covariates (which is/are chosen as columns in data). sigma is the scale parameter of the distribution.
Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari
Prabhash et al(2016) <doi: 10.21307/stattrans-2016-046>
wbysmv, lgnbymv, lgstbyuni
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data(hdata)
lgstbymv(10,12,STime="os",Event="death",5,100,hdata)
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