Description Usage Arguments Details Value Author(s) References See Also Examples

Provides posterior estimates of AFT model with weibull distribution using MCMC for univariate in high dimensional gene expression data. It also deals covariates with missing values.

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`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 status in data. 0 is for censored and 1 for occurrence of event. |

`nc` |
number of markov chain. |

`ni` |
number of iteration for MCMC. |

`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'β +σε_i ;~ε_i \sim F_ε (.)~which~is~iid *

Where * F_ε * is known cdf which is defined on real line.
Here, when baseline distribution is extreme value then T follows weibull distribution.
To make interpretation of regression coefficients simpler, using extreme value distribution with median 0.
So using weibull distribution that leads to AFT model when

* T \sim Weib(√{τ},log(2)\exp(-x'β √{τ})) *

Data frame is containing posterior estimates (Coef, SD, Credible Interval, Rhat, n.eff) of regression coefficient for 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>

pvaft, wbysmv, rglwbysu, wbyscrku

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