<p>Set <code>numberEff = True</code></p>
<pre><code>MRcoefs(fit, coef = 2, group = 3, number = 143, eff = 5.5, numberEff = True) 
</code></pre>
<p>I suspect you've got the quantile value set, hence it will throw an error above 1.</p>

CC BY-SA 4.0
2023-08-22T02:27:57.617Z
2023-08-22T13:27:35.093Z
2023-08-22T13:27:35.093Z
M__
<p>Cross-posted on biostars: <a href="https://www.biostars.org/p/9572744/" rel="nofollow noreferrer">https://www.biostars.org/p/9572744/</a></p>
<p>I am trying to understand better what number to be used and how to put it in the function MRcoefs within the Metagenomeseq package in R for calculating differentially expressed OTUs between two condition:
my code looks like:</p>
<pre><code>MRcoefs(fit, coef = 2, group = 3, number = 143, eff = 5.5) 
</code></pre>
<p>Here I am setting the eff argument to 5.5 because I have calculated the effectiv_size using this code:</p>
<pre><code>Effective_size(fit) 
</code></pre>
<p>which result in table of effective size for each feature (OTU), and then I got the mean of these</p>
<pre><code>mean(Effective_size(fit))
</code></pre>
<p>which was 5.5
When I run this, it produced an error:
prop outside [0,1], I understand it should be between 0-1, but why ? is not that number in eff is the mean that I calculated which bring 5.5 ?
Also when I tried to run it with .5 as the document suggested as below :</p>
<pre><code>MRcoefs(fit, coef = 2, group = 3, number = 143, eff = .5) 
</code></pre>
<p>it run nicely, no error , but it produced the same OTUs as when I did not include eff, I was expecting that some OTUs will be removed as they were below this threshold</p>
<p>Could you please help me ?</p>

CC BY-SA 4.0
2023-08-20T12:32:11.690Z
Hi @MohamedSamir, firstly please put your code into coding blocks. Secondly, the package you are using is **Metagenomeseq** but the tag is **16S**. These are two different approaches, could you please clarify which you are using?
M__
CC BY-SA 4.0
2023-08-21T16:05:34.823Z
Thanks for your advice. I mean Metagenomeseq which is used for analysing 16S microbiome data. I mean MetagenomeSeq package
Mohamed Samir
<r><microbial-genomics><16rrna><microbiome>
Try to understand better how to set out an effective sample size within the MetagenomSeq package in microbiome
# Try to understand better how to set out an effective sample size within the MetagenomSeq package in microbiome

(Asked by: username_0 on 2023-08-20)

Cross-posted on biostars: https://www.biostars.org/p/9572744/
I am trying to understand better what number to be used and how to put it in the function MRcoefs within the Metagenomeseq package in R for calculating differentially expressed OTUs between two condition:
my code looks like:
MRcoefs(fit, coef = 2, group = 3, number = 143, eff = 5.5) 

Here I am setting the eff argument to 5.5 because I have calculated the effectiv_size using this code:
Effective_size(fit) 

which result in table of effective size for each feature (OTU), and then I got the mean of these
mean(Effective_size(fit))

which was 5.5
When I run this, it produced an error:
prop outside [0,1], I understand it should be between 0-1, but why ? is not that number in eff is the mean that I calculated which bring 5.5 ?
Also when I tried to run it with .5 as the document suggested as below :
MRcoefs(fit, coef = 2, group = 3, number = 143, eff = .5) 

it run nicely, no error , but it produced the same OTUs as when I did not include eff, I was expecting that some OTUs will be removed as they were below this threshold
Could you please help me ?

**Tags:** r, microbial-genomics, 16rrna, microbiome
> **Comments:**
> **username_1**: Hi @MohamedSamir, firstly please put your code into coding blocks. Secondly, the package you are using is **Metagenomeseq** but the tag is **16S**. These are two different approaches, could you please clarify which you are using?  
> **username_0**: Thanks for your advice. I mean Metagenomeseq which is used for analysing 16S microbiome data. I mean MetagenomeSeq package  

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## 1 answers

### Answer by username_1 on 2023-08-22. Score: 0

Set numberEff = True
MRcoefs(fit, coef = 2, group = 3, number = 143, eff = 5.5, numberEff = True) 

I suspect you've got the quantile value set, hence it will throw an error above 1.


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