[R-sig-ME] Advice for analysis of biological data - Mixed model or NESTED-Anova?

[Evan Palmer-Young]

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=data, 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*
>
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>
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>



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