[R-sig-ME] lme for data that is not normally distributed

[moses selebatso]

 Hello
I have some data that I would to analyse with mixed models (lme). As a standard procedure I tested for the normality of the data and it is not normal. Any ideas of how deals with this kind of data? I have a sample below and the model that I was hoping to use (if the data was normal)
m <- lme(Distance~Time,random=~1|ID,data=data).


 

 
|

 
| ID |

 
| Time |

 
| Distance |

  
|

 
| 10187A |

 
| Pre_dry |

 
| 4.31287 |

  
|

 
| 10187A |

 
| Pre_dry |

 
| 6.867578 |

  
|

 
| 10187A |

 
| Pre_dry |

 
| 4.640427 |

  
|

 
| 10187A |

 
| Post_dry |

 
| 4.497807 |

  
|

 
| 10187A |

 
| Post_dry |

 
| 9.726069 |

  
|

 
| 10187A |

 
| Post_dry |

 
| 5.150089 |

 


Regards,
Moses SELEBATSO 
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