What is NMDS R?

06 Jun 2019. Non-metric Multi-dimensional Scaling (NMDS) is a way to condense information from multidimensional data (multiple variables/species/OTUs), into a 2D representation or ordination.

How do you read an NMDS graph?

As a rule of thumb, an NMDS ordination with a stress value around or above 0.2 is deemed suspect and a stress value approaching 0.3 indicates that the ordination is arbitrary. Stress values equal to or below 0.1 are considered fair, while values equal to or below 0.05 indicate good fit.

What is the difference between MDS and NMDS?

Similar to PCoA, MDS first needs to calculate a matrix of sample (objects) dissimilarities using a chosen distance metric, while NMDS calculates the ranks of these distances among all samples (objects).

What does an NMDS plot tell you?

The goal of NMDS is to represent the original position of data in multidimensional space as accurately as possible using a reduced number of dimensions that can be easily plotted and visualized (like PCA). BUT (unlike PCA which uses Euclidian distances) NMDS relies on rank orders.

What is MDS vs PCA?

PCA is just a method while MDS is a class of analysis. As mapping, PCA is a particular case of MDS. On the other hand, PCA is a particular case of Factor analysis which, being a data reduction, is more than only a mapping, while MDS is only a mapping.

What is the difference between PCA and RDA?

PCA and RDA are very similar is what they do. Although, they differ as PCA is unconstrained (search for any variable that best explains spp composition), whereas RDA is constrained (search for the best explanatory variables). It depends on the gradient lengths (tested with a DCA or DCCA).

Does MDS preserve distances?

In general, the metric MDS calculates distances between each pair of points in the original high-dimensional space and then maps it to lower-dimensional space while preserving those distances between points as well as possible. Note, the number of dimensions for the lower-dimensional space can be chosen by you.

What is Shepard diagram?

A Shepard diagram compares how far apart your data points are before and after you transform them (ie: goodness-of-fit) as a scatter plot. Shepard diagrams can be used for data reduction techniques like principal components analysis (PCA), multidimensional scaling (MDS), or t-SNE.