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

[Highland Statistics Ltd]

> Date: Wed, 3 Aug 2016 09:40:20 +0000 (UTC)
> From: moses selebatso <selebatsom at yahoo.co.uk>
> To: R-sig-mixed-models <r-sig-mixed-models at r-project.org>
> Subject: [R-sig-ME] lme for data that is not normally distributed
> Message-ID:
> 	<127496753.15122202.1470217220406.JavaMail.yahoo at mail.yahoo.com>
> Content-Type: text/plain; charset="UTF-8"
>
> ?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).


Checking normality of the response variable before doing the analysis is 
a misconception. Why should it be normally distributed? Fit your model 
and check your residuals for normality.


Alain

>
>   
>
>   
> |
>
>   
> | 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?
> 	[[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
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> ------------------------------
>
> End of R-sig-mixed-models Digest, Vol 116, Issue 4
> **************************************************
>

-- 
Dr. Alain F. Zuur

First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).

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