<p>There are a lot of factors that contribute to taking variance away from the main terms in a model that could explain your result.</p>
<p>When adding more factors, and especially interactions with <code>Line</code>, the new terms explain some of the variance that was initially explained by just <code>Line</code>. So it looks not significant.</p>
<p>You can also have collinear individual terms that share the variance that was initially attributed to <code>Line</code>. You can check for collinearity by seeing if any of the individual terms are significantly correlated.</p>
<p>That said, you should look into simplifying your model by iteratively removing non-significant terms, starting with the largest interactions. Eventually, when you get to your minimum adequate model, you may find <code>Line</code> is significant again. But you should also check for things like collinearity.</p>
<p>Maybe this will also help. There is a great discussion <a href="https://stats.stackexchange.com/questions/20452/how-to-interpret-type-i-type-ii-and-type-iii-anova-and-manova">here</a> about how variance is explained between type II and type III ANOVAs that really helped me understand model outputs in R, even if it's not the same model.</p>

CC BY-SA 4.0
2024-10-10T14:17:27.713Z
2024-10-10T14:17:27.713Z
dthorbur
<p>I ran multivariate analysis using the mvabund package on R to see which factors were significant in influencing gut microbiota. When I was running it for just the factor &quot;Line&quot;, my results were significant. However, when I ran for Line x Larval Age x Diet, the table shows me that &quot;Line&quot; is no longer a significant factor? Hope someone can help me to understand this better or correct me if I am wrong. Thank you. <a href="https://i.sstatic.net/oTIxEw3A.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oTIxEw3A.png" alt="enter image description here" /></a>
<a href="https://i.sstatic.net/lQ08vuh9.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/lQ08vuh9.png" alt="enter image description here" /></a></p>

<r><rna-seq><microbial-genomics><microbiology><microbiome>
Interpretation of MVABUND results
# Interpretation of MVABUND results

(Asked by: username_0 on 2024-10-09)

I ran multivariate analysis using the mvabund package on R to see which factors were significant in influencing gut microbiota. When I was running it for just the factor "Line", my results were significant. However, when I ran for Line x Larval Age x Diet, the table shows me that "Line" is no longer a significant factor? Hope someone can help me to understand this better or correct me if I am wrong. Thank you. [![enter image description here](https://i.sstatic.net/oTIxEw3A.png)](https://i.sstatic.net/oTIxEw3A.png)
[![enter image description here](https://i.sstatic.net/lQ08vuh9.png)](https://i.sstatic.net/lQ08vuh9.png)

**Tags:** r, rna-seq, microbial-genomics, microbiology, microbiome

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## 1 answers

### Answer by username_1 on 2024-10-10. Score: 0 | Accepted answer

There are a lot of factors that contribute to taking variance away from the main terms in a model that could explain your result.

When adding more factors, and especially interactions with `Line`, the new terms explain some of the variance that was initially explained by just `Line`. So it looks not significant.

You can also have collinear individual terms that share the variance that was initially attributed to `Line`. You can check for collinearity by seeing if any of the individual terms are significantly correlated.

That said, you should look into simplifying your model by iteratively removing non-significant terms, starting with the largest interactions. Eventually, when you get to your minimum adequate model, you may find `Line` is significant again. But you should also check for things like collinearity.

Maybe this will also help. There is a great discussion [here](https://stats.stackexchange.com/questions/20452/how-to-interpret-type-i-type-ii-and-type-iii-anova-and-manova) about how variance is explained between type II and type III ANOVAs that really helped me understand model outputs in R, even if it's not the same model.


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