lgstbyuni {afthd} | R Documentation |

## Univariate estimates of AFT model with log logistic distribution using MCMC.

### Description

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.

### Usage

```
lgstbyuni(m, n, STime, Event, nc, ni, data)
```

### Arguments

`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. |

### Details

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)} `

### Value

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.

### Author(s)

Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari

### References

Prabhash et al(2016) <doi:10.21307/stattrans-2016-046>

### See Also

wbysmv, lgnbymv, lgstbymvs

### Examples

```
##
data(hdata)
lgstbyuni(12,14,STime="os",Event="death",3,100,hdata)
##
```

*afthd*version 1.1.0 Index]