I'm not sure how you are incorporating time period into your data structure.
Typically we are looking at plots or assemblages as the rows and taxa as the
columns. Time period adds a third dimension that could be added as blocks of
rows. For example, depending on the resolution of your data, one approach would
be to have up to 8 rows for each locality: Loc1.1000, Loc1.2000, . . .
Loc1.8000. If a locality did not have certain millennia represented you would
just leave them out.
David C
-----Original Message-----
From: Jessie Woodbridge <jessie.woodbridge at plymouth.ac.uk>
Sent: Wednesday, April 18, 2018 3:42 AM
To: David L Carlson <dcarlson at tamu.edu>
Subject: RE: nMDS with R: missing values
Dear Prof Carlson,
Thank you for your reply. I'm using 'vegan' with 'vegdist'
and 'bray'. I have a selection of datasets that cover different time
periods (converted to z-scores), so a record that starts 5000 years ago would
have missing data before this date when other records cover the last 8000 years.
I need to build into the nMDS the fact that this isn't a zero score, but
reflects absence of data. In the results it seems that datasets that cover the
same time periods are being grouped together because they have zero values at
the same times. I hope that makes sense. Any suggestions would be greatly
appreciated.
There is no regular pattern to the position of the missing values, so I
can't remove certain rows or columns. Thank you for the suggestion of using
dist() and na.rm=TRUE. I will try using these approaches.
Kind regards,
Jessie
********************
Dr Jessie Woodbridge
Geography, Earth and Environmental Sciences B416, Portland Square, University of
Plymouth, UK
01752 585920
Leverhulme Trust-funded ?Changing the face of the Mediterranean? project
Leverhulme Trust-funded Deforesting Europe project
-----Original Message-----
From: David L Carlson <dcarlson at tamu.edu>
Sent: 17 April 2018 21:13
To: Jessie Woodbridge <jessie.woodbridge at plymouth.ac.uk>; r-help at
r-project.org
Subject: RE: nMDS with R: missing values
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-bounces at r-project.org> On Behalf Of Jessie
Woodbridge
Sent: Tuesday, April 17, 2018 2:05 PM
To: r-help at 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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responsibility to scan emails and their attachments. Plymouth University does
not accept responsibility for any changes made after it was sent. Nothing in
this email or its attachments constitutes an order for goods or services unless
accompanied by an official order form.