lme for data that is not normally distributed

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]]



------------------------------

Subject: Digest Footer

-- 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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