<p>I'm using <code>adonis</code>, an implementation of MANOVA in R (<code>vegan</code> package) to understand how continuous (temperature, pH) and categorical (Site ID, Month) variables affect my 16S dataset. Applying it using my metadata 'as-is', I see that ~80% of the variation (R2) is significantly explained by one variable - Site ID.</p>

<p>However, after suggestions from colleagues to normalize my data (as shown <a href="http://vitalflux.com/data-science-scale-normalize-numeric-data-using-r" rel="nofollow noreferrer">here</a> for 'min-max' normalization), the same test not only shows Site ID as the third most important variable (in explaining the most variation significantly), it only shows ~31% variation being explained from the first three variables.</p>

<p>Any opinions on best practices for this kind of analysis?</p>

CC BY-SA 3.0
2018-03-05T14:36:01.237Z
Hi and welcome to the site. I'm afraid cross posting is very much [frowned upon](https://meta.stackoverflow.com/questions/64068/) on the SE network. Please either delete your question from one of the two sites, or edit it so that it is specifically addressed to the subject matter of each site. For instance, for us, we would need to know what kind of data you're dealing with ("16S dataset" isn't very specific).
terdon
CC BY-SA 3.0
2018-03-05T15:50:58.920Z
Also what kind of normalization did you do?  What test are you using? t-test or some package normalization and test procedure?
llrs
CC BY-SA 3.0
2018-03-05T16:20:48.367Z
@Llopis that would be a "min-max" normalization, as what's found here: https://vitalflux.com/data-science-scale-normalize-numeric-data-using-r/
adonis is documented here: http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/adonis.html
It uses something directly relatable to a MANOVA.
André Soares
CC BY-SA 3.0
2018-03-05T16:24:19.193Z
@AndréSoares Could you edit that comment into your question? Also if you could add what kind of data are you using (like terdon asked)? Why did you choose the "min-max" normalization?
llrs
<r>
Normalize before using adonis?
# Normalize before using adonis?

(Asked by: username_0 on 2018-03-05)

I'm using `adonis`, an implementation of MANOVA in R (`vegan` package) to understand how continuous (temperature, pH) and categorical (Site ID, Month) variables affect my 16S dataset. Applying it using my metadata 'as-is', I see that ~80% of the variation (R2) is significantly explained by one variable - Site ID.

However, after suggestions from colleagues to normalize my data (as shown [here](http://vitalflux.com/data-science-scale-normalize-numeric-data-using-r) for 'min-max' normalization), the same test not only shows Site ID as the third most important variable (in explaining the most variation significantly), it only shows ~31% variation being explained from the first three variables.

Any opinions on best practices for this kind of analysis?

**Tags:** r
> **Comments:**
> **username_1**: Hi and welcome to the site. I'm afraid cross posting is very much [frowned upon](https://meta.stackoverflow.com/questions/64068/) on the SE network. Please either delete your question from one of the two sites, or edit it so that it is specifically addressed to the subject matter of each site. For instance, for us, we would need to know what kind of data you're dealing with ("16S dataset" isn't very specific).  
> **username_2**: Also what kind of normalization did you do?  What test are you using? t-test or some package normalization and test procedure?  
> **username_0**: @Llopis that would be a "min-max" normalization, as what's found here: https://vitalflux.com/data-science-scale-normalize-numeric-data-using-r/ adonis is documented here: http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/adonis.html It uses something directly relatable to a MANOVA.  
> **username_2**: @AndréSoares Could you edit that comment into your question? Also if you could add what kind of data are you using (like terdon asked)? Why did you choose the "min-max" normalization?  

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