I think you will have to provide some more information. What function/package 
are you using for nMDS (eg. isoMDS in MASS, monoMDS in vegan)? What 
function/package are you using to compute your distance/dissimilarities (eg. 
dist in stats, vegdist in vegan)? 

Zero represents absence. It is not a missing value, so that part of your 
question is not clear.

The dist() function computes distance ignoring missing values if they are 
properly represented as NAs in the data. The vegdist() function does this if 
na.rm=TRUE. 

The results will be affected by how much data is missing. It would be useful to 
know if the missing values are concentrated in particular rows or columns so 
that eliminating a few rows and columns could substantially reduce the 
percentage of missing values.

----------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77843-4352


0-----Original Message-----
From: R-help <r-help-boun...@r-project.org> On Behalf Of Jessie Woodbridge
Sent: Tuesday, April 17, 2018 2:05 PM
To: r-help@r-project.org
Subject: [R] nMDS with R: missing values

Dear All,

I was wondering whether anyone might be able to provide some advice with an 
nMDS / R problem. I’m trying to run nMDS on a dataset that contains many 
missing values and was wondering how I can account for the missing values when 
running nMDS? It seems as though the data are being grouped depending on where 
the zero values appear. Any suggestions greatly appreciated. Thank you very 
much in advance.

Apologies if this message isn’t relevant to you.

Kind regards,

Jessie

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