[R-sig-ME] lme for data that is not normally distributed
moses selebatso
selebatsom at yahoo.co.uk
Wed Aug 3 21:58:23 CEST 2016
Thank you both Paul and Alain for your help. You both point out that I shouldn't test for normality before running a model. I appreciate that. Paul I have tried you new scripts and, I guess you were right about experience in visually assessing for normality. Not straight forward. Below is the plot, for your appreciation.
library(lme4)
install.packages("devtools")
library(devtools)
devtools::install_github("pcdjohnson/GLMMmisc")
library(GLMMmisc)
data<-read.csv("clipboard",sep="\t")
m <- lmer(Distance ~ Time + (1 | ID), data = data)
sim.residplot(m)
Regards,
Moses SELEBATSO Home: (+267) 318 5219 (H) Mobile: (+267) 716 39370 or (+267) 738 39370"Those who will ALWAYS agree with you may be oppressed by you"
On Wednesday, 3 August 2016, 15:54, Paul Johnson <paul.johnson at glasgow.ac.uk> wrote:
Hi Moses,
I wouldn’t test normality of residuals — better to assess them by eye. I know this sounds ad hoc but given that almost no real distribution in nature is perfectly normal, the question should be “how non-normal can the residuals be before seriously harming my inferences?”. This is a more difficult question to answer and basically requires experience. A test conflates the degree of non-normality and sample size so a significant result can mean “quite normal but high n” while a non-significant result can mean “very non-normal but low n”:
set.seed(1)
x <- rpois(1000, 50)
hist(x) # looks beautifully normal
shapiro.test(x) # significantly non-normal
hist(log(x[1:20])) # looks pretty bad
shapiro.test(log(x[1:20])) # passes the test
Given that your distance response measure is (probably) constrained to be positive, there’s a good change that it’s right-skewed and potentially made more normal by log-transformation (if there are no zero distances).
A good way to visually assess residuals is to plot them against the fitted values, then compare these to residuals simulated from the fitted model — they should look similar, give or take sampling variation. You can do this with a function I recently wrote called sim.residplot (available here: https://github.com/pcdjohnson/GLMMmisc), although you’ll have to refit your model using lmer in the lme4 package:
library(lme4)
library(GLMMmisc)
m <- lmer(Distance ~ Time + (1 | ID), data = data)
sim.residplot(m) # repeat a few times to allow for sampling variation
Good luck,
Paul
> On 3 Aug 2016, at 14:25, moses selebatso via R-sig-mixed-models <r-sig-mixed-models at r-project.org> wrote:
>
> Thank very much for your helpful advice. I ran the model and tested the residuals. They are not normally distributed, and I am still stuck with how I proceed. I tried to copy the output on the email, but I get an error message that the message format cannot sent.
> Regards,
> Moses
>
> On Wednesday, 3 August 2016, 12:15, Highland Statistics Ltd <highstat at highstat.com> wrote:
>
>
>
>
>
>> 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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>
> --
> 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).
>
> Highland Statistics Ltd.
> 9 St Clair Wynd
> UK - AB41 6DZ Newburgh
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