[R-sig-ME] Advice for analysis of biological data - Mixed model or NESTED-Anova?
Evan Palmer-Young
epalmery at cns.umass.edu
Wed Jun 1 00:20:27 CEST 2016
Savani,
I think your problem is that your model is using group as both fixed and
random effect.
Try this instead:
So for your experiments with NO treatment, you need a model to assess the
effect of group, if you want to say, "Did the groups differ":
simple_model<-nlme(logVolume ~ Group , random= ~Animal_ID, data=savanidata)
For your experiments WITH a treatment,
treatment_model<-nlme(logVolume ~ Treatment , random= ~Group/Animal_ID,
data=savanidata)
If you actually want to know the interaction between treatment and group,
then you must have "Group" as fixed effect:
interaction_model<-nlme(logVolume ~ Treatment*Group , random= ~Animal_ID,
data=savanidata)
Then you can test the terms like this:
model1<- update(interaction_model, ~. - Treatment:Group) #excludes
interaction
anova(interaction_model, model1) #likelihood ratio test; if p<0.05, the
interaction term is doing a good job in your model, so you should keep the
interaction term
Otherwise, you can keep simplifying, like this:
model2<-update(model1, ~. - Group)
anova(model1, model2)
And so you can test the importance of each term.
Happy modeling,
Evan
On Thu, May 26, 2016 at 5:17 AM, Savani Anbalagan <savani1987 at gmail.com>
wrote:
> Hi Evan,
>
> Thanks a lot.
> When you say the biggest group. I assume it is just about the number of
> animals in the group. Not about about the total number of observations for
> a group.
>
> And, when I run the code, I get the error
>
>
> brainmodel<-nlme(logVolume ~ Group , random= ~Group/Animal_ID,
>
> data=dat)
> Error in model[[3]][[1]] : object of type 'symbol' is not subsettable
>
> And
>
> In the full or reduced models with lme4: What might be an reduced model in
> my case?
> Because, in expt design 1 : I only have 3 groups.
>
>
> thanks,
> Savani
>
>
>
> On 25 May 2016 at 21:58, Evan Palmer-Young <epalmery at cns.umass.edu> wrote:
>
> > Dear Savani,
> > I think you are on the right track. If you use function nlme, you can get
> > your p-values straightaway.
> > With lme4, you have to employ another function (Likelihood ratio test on
> > full and reduced models, or Wald tests with Anova in car) to extract
> them:
> > see:
> > http://www.inside-r.org/packages/cran/lme4/docs/pvalues
> >
> > For your model coding, make sure that the biggest group is listed FIRST.
> > So for you:
> > model2=lmer(logVolume ~ Group + (1|Animal_ID/Group ),
> >
> > data=dat, REML =
> > FALSE)
> > Instead use
> >
> > brainmodel<-nlme(logVolume ~ Group , random= ~Group/Animal_ID)
> > See some examples under "model specification" on this very helpful page:
> > http://glmm.wikidot.com/faq
> >
> > Here are some nlme examples:
> > http://www.stat.ubc.ca/~lang/Stat527a/ex4.r
> >
> > Good luck!
> >
> > On Wed, May 25, 2016 at 3:32 PM, Savani Anbalagan <savani1987 at gmail.com>
> > wrote:
> >
> >> Dear all,
> >>
> >> I was suggested in the stack exchange.com to consult in this maling
> list.
> >>
> >> I have data from image analysis of zebrafish brain structures. I will
> >> discuss our data below with some analogy to make my explanation clear.
> >>
> >> 1. Data model: Group>Animal 1..2...3....10>Volume 1..2..3.....1000
> >> 2. Data model: Group>Drug treatment..1..2>Animal
> 1..2...3....10>Volume
> >> 1..2..3.....1000
> >> 3. I am studying axonal synapses in Brain.
> >> 4. I have 3 or more groups (Genotypes: Wild type, Hetero, Homozygous
> >> mutant)
> >> 5. Animals are sacrificied to image them.
> >> 6. I have 10+ animals from each group.
> >> 7. The number and volume of the synapses are variable.
> >> 8. Within the group, some animals have 300 synapses, some have 450
> >> synapses.
> >> 9. The volume of the synapses range from 0.2 to 50. The histrogram is
> >> highly skewed towards lower values. A log transformation makes it
> look
> >> more
> >> normal.
> >> 10. Some times, we also treat the different groups to a drug. So, it
> >> makes another level.
> >> 11.
> >>
> >> Analogy:
> >>
> >> 1. > (Imagine a tree with fruits of different sizes. And I am
> >> interested
> >> in the size of the fruits)
> >> 2. >(Lets say, I have trees of different species. example Indian
> Mango
> >> vs Brazilian Mango vs another Mango)
> >> 3. >(To collect fruits, The trees are cut. )
> >> 4. >(10+ trees in each groups)
> >> 5. >(The number of fruits vary depending on tree to tree even within
> >> same group. The size of the fruit varies. There are relatively too
> many
> >> small fruits).
> >> 6. >(Some times, fertilizers are added to tree, and then effect of
> >> fruit
> >> count/size is also checked)
> >>
> >> My questions:
> >> Could you please let me know,
> >>
> >>
> >> 1. Should I perform Nested ANOVA or Mixed model analysis?
> >> 2. If mixed model design, should I run the analysis on log
> transformed
> >> data or raw data? Is the distribution important for mixed model
> >> analysis?
> >> 3. If drug treatment is added, Is it Nested or Mixed model design?
> >> 4. For mixed model analysis how can I calculate p-value? Could you
> >> please let me know for both the cases. For experiments, without any
> >> drug.
> >> And for experiments with the drug treated vs control.
> >> 5. These are the codes that I use to analyze my data: Could you check
> >> if
> >> it is correct?
> >>
> >>
> >> My nested anova code I use:
> >> logGFPVol.anova = aov(logVolume ~ Group + Error(Animal_ID/Group),
> >> data=data)
> >> summary(logGFPHBVol.anova)
> >>
> >>
> >> Mixed model code:
> >> model2=lmer(logVolume ~ Group + (1|Animal_ID/Group ), data=data, REML =
> >> FALSE)
> >> summary(model2)
> >>
> >>
> >> Please feel free to ask if I am unclear.
> >>
> >> Many thanks,
> >> Savani
> >>
> >> --------------------------------------------------------
> >>
> >> *Savani Anbalagan, Ph.D*
> >>
> >> *Dept. of Mol. Cell Biology*
> >>
> >>
> >> *Weizmann Institute of Science234 Herzl St., Rehovot 76100,*
> >>
> >>
> >> *ISRAELPhone: +972-8934-6158*
> >>
> >> [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
> >> R-sig-mixed-models at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>
> >
> >
> >
> > --
> > Department of Biology
> > 221 Morrill Science Center
> > 611 North Pleasant St
> > Amherst MA 01003
> > https://sites.google.com/a/cornell.edu/evan-palmer-young/
> >
>
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>
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Department of Biology
221 Morrill Science Center
611 North Pleasant St
Amherst MA 01003
https://sites.google.com/a/cornell.edu/evan-palmer-young/
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