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

Ben Bolker bbolker at gmail.com
Wed Aug 3 22:14:46 CEST 2016 

   For what it's worth, this graph is assessing
linearity/heteroscedasticity rather than Normality (you would want a Q-Q
plot, not a fitted vs residuals plot, for that).  This doesn't look too
terrible, but there does seem to be a bit of 'flare' at the
large-fitted-value end, which supports Paul's suggestion that you try a
log transformation ...

On 16-08-03 03:58 PM, moses selebatso via R-sig-mixed-models wrote:
> 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 
>> Tel:  0044 1358 788177 Email: highstat at highstat.com URL:
>> www.highstat.com
>> 
>> _______________________________________________ 
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