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inst/pages/beta_diversity.qmd

@@ -124,7 +124,6 @@ are distinct. If this assumption isn’t met, it can affect the interpretation o
 the results, so it’s important to check this assumption before drawing
 conclusions.
 
-
 ```{r}
 #| label: permanova
 
@@ -136,8 +135,6 @@ res <- getPERMANOVA(
 res
 ```
 
-Here’s a refined version of your text with improved clarity and flow:
-
 The output includes both PERMANOVA and homogeneity results. When PERMANOVA
 results are significant, we check the homogeneity results to ensure reliability.
 A non-significant homogeneity result indicates that group dispersions are
@@ -148,6 +145,12 @@ In our case, PERMANOVA results are significant, and the homogeneity test shows
 that group dispersion is indeed homogeneous. This suggests that _SampleType_
 explains the variance in the microbial profile.
 
+PERMANOVA and distance-based redundancy analysis (dbRDA) are closely related,
+and often they give similar results. However, their assumptions are different;
+while PERMANOVA is non-parametric method, dbRDA assumes linear relationships in
+dissimilarity matrix. However, dbRDA can be used more broadly as the ordination
+can also be visualized.
+
 ## Unsupervised ordination {#sec-unsupervised-ordination}
 
 Unsupervised ordination methods analyze variation in the data without additional