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

[Savani Anbalagan]

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*
>>
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>>
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>>
>
>
>
> --
> Department of Biology
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> https://sites.google.com/a/cornell.edu/evan-palmer-young/
>

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