Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
Alan,
Use vegan command
vegandocs("decision")
and look at the chapter on scaling of RDA scaling. This explains both the
scaling and how to change the scaling.
Cheers, Jari Oksanen
From: r-sig-ecology-boun...@r-project.org [r-sig-ecology-boun...@r-project.org]
on behalf of Alan Haynes [aghay...@gmail.com]
Sent: 10 May 2012 16:43
To: Martin Weiser
Cc: r-sig-ecology
Subject: Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript
reviewers
Thanks both, Im happy to have my thoughts confirmed.
Gavin, I ran all of the variables I included through ordisurf aswell as
envfit to see if there were non-linearities, and, in all bar a single case,
the surface isobars happened to be straight. As it turns out, the one that
wasnt linear didnt make sense in terms of the analysis in any case.
A question perhaps more for Jari, the PCA results (eigenvalues and vectors,
but not rank) from rda seem to be slightly different in terms of scale than
other functions (e.g. labdsv:::pca which wraps stats:::prcomp i believe).
Plots look identical, except for the size of the axis scores - rda scores
seem to be approximately 1/5th of pca scores. Why is that? Is there a
solution to rescale them?
Thanks again,
Alan
--
Email: aghay...@gmail.com
Mobile: +41794385586
Skype: aghaynes
On 10 May 2012 14:57, Martin Weiser wrote:
> Hi Alan,
> I think that PCA is even better with envfit than NMDS with envfit. This
> is because PCA works in linear euclidean world, so correlation makes
> better sense in this case. You are correlating points on lines (envfit)
> with points on lines (PCA), rather than points on lines (envfit) with
> undetermined something non-regularly stressed (NMDS).
> But this is just my feeling, I may be wrong easily, but in that case I
> hope someone will correct me.
> Best,
> Martin Weiser
>
> Alan Haynes píše v Čt 10. 05. 2012 v 13:17 +0200:
> > Hi all,
> >
> > Im using envfit with some decomposition data currently but with a PCA
> > result (via vegan:::rda()). Is envfit still valid for PCA results? I
> guess
> > it doesnt make so very much difference, just the interpretation is
> slightly
> > different.
> > Or am I barking up the wrong tree by using this approach?
> >
> > Cheers,
> >
> > Alan
> >
> > --
> > Email: aghay...@gmail.com
> > Mobile: +41794385586
> > Skype: aghaynes
> >
> >
> > On 10 May 2012 12:53, Gavin Simpson wrote:
> >
> > > I've removed R-Help from this now...
> > >
> > > On Thu, 2012-05-10 at 10:13 +, Jari Oksanen wrote:
> > > > On 10/05/2012, at 11:45 AM, Gavin Simpson wrote:
> > >
> > > > > As you provide little or no context I'll explain what envfit() does
> > > etc.
> > > > >
> > > > > The idea goes back a long way (!) and is in my 1995 edition of
> Jongman
> > > > > et al Data Analysis in Community and Landscape Ecology (Cambridge
> > > > > University Press) though most likely was in 1987 version too. See
> > > > > Section 5.4 of the Ordination chapter by Ter Braak in that book.
> > > > >
> > > > > The idea is to find the direction (in the k-dimensional ordination
> > > > > space) that has maximal correlation with an external variable.
> > > >
> > > >
> > > > Hello,
> > >
> > >
> > >
> > > > Then about Bray-Curtis. The referee may be correct when writing that
> > > > the fitted vectors are not directly related to Bray-Curtis. You fit
> > > > the vectors to the NMDS ordination, and that is a non-linear mapping
> > > > from Bray-Curtis to the metric ordination space. There are two
> points
> > > > here: non-linearity and stress. Because of these, it is not strictly
> > > > about B-C. Of course, the referee is wrong when writing about NMDS
> > > > axes: the fitted vector has nothing to do with axes (unless you
> rotate
> > > > your axis parallel to the fitted vector which you can do). The NMDS
> is
> > > > based on Bray-Curtis, but it is not the same, and the vector fitting
> > > > is based on NMDS. So why not write that is about NMDS? Why to insist
> > > > on Bray-Curtis which is only in the background?
> > >
> > > Right, agreed. The analysis is one step removed from the B-C but the
> > > point of doing the nMDS was to find a low-d mapping of these B-C
> > > distances so in the sense that *if* the mapping is a good one
Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
Thanks both, Im happy to have my thoughts confirmed. Gavin, I ran all of the variables I included through ordisurf aswell as envfit to see if there were non-linearities, and, in all bar a single case, the surface isobars happened to be straight. As it turns out, the one that wasnt linear didnt make sense in terms of the analysis in any case. A question perhaps more for Jari, the PCA results (eigenvalues and vectors, but not rank) from rda seem to be slightly different in terms of scale than other functions (e.g. labdsv:::pca which wraps stats:::prcomp i believe). Plots look identical, except for the size of the axis scores - rda scores seem to be approximately 1/5th of pca scores. Why is that? Is there a solution to rescale them? Thanks again, Alan -- Email: aghay...@gmail.com Mobile: +41794385586 Skype: aghaynes On 10 May 2012 14:57, Martin Weiser wrote: > Hi Alan, > I think that PCA is even better with envfit than NMDS with envfit. This > is because PCA works in linear euclidean world, so correlation makes > better sense in this case. You are correlating points on lines (envfit) > with points on lines (PCA), rather than points on lines (envfit) with > undetermined something non-regularly stressed (NMDS). > But this is just my feeling, I may be wrong easily, but in that case I > hope someone will correct me. > Best, > Martin Weiser > > Alan Haynes pÃÅ¡e v Ät 10. 05. 2012 v 13:17 +0200: > > Hi all, > > > > Im using envfit with some decomposition data currently but with a PCA > > result (via vegan:::rda()). Is envfit still valid for PCA results? I > guess > > it doesnt make so very much difference, just the interpretation is > slightly > > different. > > Or am I barking up the wrong tree by using this approach? > > > > Cheers, > > > > Alan > > > > -- > > Email: aghay...@gmail.com > > Mobile: +41794385586 > > Skype: aghaynes > > > > > > On 10 May 2012 12:53, Gavin Simpson wrote: > > > > > I've removed R-Help from this now... > > > > > > On Thu, 2012-05-10 at 10:13 +, Jari Oksanen wrote: > > > > On 10/05/2012, at 11:45 AM, Gavin Simpson wrote: > > > > > > > > As you provide little or no context I'll explain what envfit() does > > > etc. > > > > > > > > > > The idea goes back a long way (!) and is in my 1995 edition of > Jongman > > > > > et al Data Analysis in Community and Landscape Ecology (Cambridge > > > > > University Press) though most likely was in 1987 version too. See > > > > > Section 5.4 of the Ordination chapter by Ter Braak in that book. > > > > > > > > > > The idea is to find the direction (in the k-dimensional ordination > > > > > space) that has maximal correlation with an external variable. > > > > > > > > > > > > Hello, > > > > > > > > > > > > > Then about Bray-Curtis. The referee may be correct when writing that > > > > the fitted vectors are not directly related to Bray-Curtis. You fit > > > > the vectors to the NMDS ordination, and that is a non-linear mapping > > > > from Bray-Curtis to the metric ordination space. There are two > points > > > > here: non-linearity and stress. Because of these, it is not strictly > > > > about B-C. Of course, the referee is wrong when writing about NMDS > > > > axes: the fitted vector has nothing to do with axes (unless you > rotate > > > > your axis parallel to the fitted vector which you can do). The NMDS > is > > > > based on Bray-Curtis, but it is not the same, and the vector fitting > > > > is based on NMDS. So why not write that is about NMDS? Why to insist > > > > on Bray-Curtis which is only in the background? > > > > > > Right, agreed. The analysis is one step removed from the B-C but the > > > point of doing the nMDS was to find a low-d mapping of these B-C > > > distances so in the sense that *if* the mapping is a good one then we > > > can talk about correlations between "distances" between sites and the > > > environmental variables. Whilst it might be strictly more correct to > > > talk about this from the point of view of the nMDS the implication is > > > that for significant envfit()s there is a significant linear > correlation > > > between the environmental variable(s) and the approximate ranked > > > distances between samples. > > > > > > I mean, if all we talk about is the nMDS who cares? it is the > > > implications of this for the system under study that are of interest. > > > > > > That said, B-C is just one of many ways to think of distance so to my > > > mind I wouldn't even talk about the B-C distance either; the interest > is > > > in differences between sites/samples. The relevance of B-C or some > other > > > coefficient only comes in when considering if they are a good > descriptor > > > of the "distance" between samples for the variables you are > considering. > > > > > > Cheers, > > > > > > G > > > > > > > Cheers, Jari Oksanen > > > > > > > > > > -- > > > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
Hi Alan, I think that PCA is even better with envfit than NMDS with envfit. This is because PCA works in linear euclidean world, so correlation makes better sense in this case. You are correlating points on lines (envfit) with points on lines (PCA), rather than points on lines (envfit) with undetermined something non-regularly stressed (NMDS). But this is just my feeling, I may be wrong easily, but in that case I hope someone will correct me. Best, Martin Weiser Alan Haynes píše v Čt 10. 05. 2012 v 13:17 +0200: > Hi all, > > Im using envfit with some decomposition data currently but with a PCA > result (via vegan:::rda()). Is envfit still valid for PCA results? I guess > it doesnt make so very much difference, just the interpretation is slightly > different. > Or am I barking up the wrong tree by using this approach? > > Cheers, > > Alan > > -- > Email: aghay...@gmail.com > Mobile: +41794385586 > Skype: aghaynes > > > On 10 May 2012 12:53, Gavin Simpson wrote: > > > I've removed R-Help from this now... > > > > On Thu, 2012-05-10 at 10:13 +, Jari Oksanen wrote: > > > On 10/05/2012, at 11:45 AM, Gavin Simpson wrote: > > > > > > As you provide little or no context I'll explain what envfit() does > > etc. > > > > > > > > The idea goes back a long way (!) and is in my 1995 edition of Jongman > > > > et al Data Analysis in Community and Landscape Ecology (Cambridge > > > > University Press) though most likely was in 1987 version too. See > > > > Section 5.4 of the Ordination chapter by Ter Braak in that book. > > > > > > > > The idea is to find the direction (in the k-dimensional ordination > > > > space) that has maximal correlation with an external variable. > > > > > > > > > Hello, > > > > > > > > > Then about Bray-Curtis. The referee may be correct when writing that > > > the fitted vectors are not directly related to Bray-Curtis. You fit > > > the vectors to the NMDS ordination, and that is a non-linear mapping > > > from Bray-Curtis to the metric ordination space. There are two points > > > here: non-linearity and stress. Because of these, it is not strictly > > > about B-C. Of course, the referee is wrong when writing about NMDS > > > axes: the fitted vector has nothing to do with axes (unless you rotate > > > your axis parallel to the fitted vector which you can do). The NMDS is > > > based on Bray-Curtis, but it is not the same, and the vector fitting > > > is based on NMDS. So why not write that is about NMDS? Why to insist > > > on Bray-Curtis which is only in the background? > > > > Right, agreed. The analysis is one step removed from the B-C but the > > point of doing the nMDS was to find a low-d mapping of these B-C > > distances so in the sense that *if* the mapping is a good one then we > > can talk about correlations between "distances" between sites and the > > environmental variables. Whilst it might be strictly more correct to > > talk about this from the point of view of the nMDS the implication is > > that for significant envfit()s there is a significant linear correlation > > between the environmental variable(s) and the approximate ranked > > distances between samples. > > > > I mean, if all we talk about is the nMDS who cares? it is the > > implications of this for the system under study that are of interest. > > > > That said, B-C is just one of many ways to think of distance so to my > > mind I wouldn't even talk about the B-C distance either; the interest is > > in differences between sites/samples. The relevance of B-C or some other > > coefficient only comes in when considering if they are a good descriptor > > of the "distance" between samples for the variables you are considering. > > > > Cheers, > > > > G > > > > > Cheers, Jari Oksanen > > > > > > > -- > > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > > Dr. Gavin Simpson [t] +44 (0)20 7679 0522 > > ECRC, UCL Geography, [f] +44 (0)20 7679 0565 > > Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk > > Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ > > UK. WC1E 6BT. [w] http://www.freshwaters.org.uk > > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > > > > ___ > > R-sig-ecology mailing list > > R-sig-ecology@r-project.org > > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > > [[alternative HTML version deleted]] > > ___ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
On Thu, 2012-05-10 at 13:17 +0200, Alan Haynes wrote: > Hi all, > > Im using envfit with some decomposition data currently but with a PCA > result (via vegan:::rda()). Is envfit still valid for PCA results? I > guess it doesnt make so very much difference, just the interpretation > is slightly different. > Or am I barking up the wrong tree by using this approach? It is perfectly valid and is introduced in Jongman et al alongside PCA and CA. We (well Jari) wouldn't have written a method for objects of class "cca" if it wasn't appropriate. I suggest you look at ordisurf() though; in most of the projects I have been involved in, the linearity assumption of envfit() is questionable. If you want a bit more info on what ordisurf() is doing see my blog post on the function: http://wp.me/pZRQ9-1x HTH G > Cheers, > > Alan > > -- > Email: aghay...@gmail.com > Mobile: +41794385586 > Skype: aghaynes > > > On 10 May 2012 12:53, Gavin Simpson wrote: > I've removed R-Help from this now... > > On Thu, 2012-05-10 at 10:13 +, Jari Oksanen wrote: > > On 10/05/2012, at 11:45 AM, Gavin Simpson wrote: > > > > > As you provide little or no context I'll explain what > envfit() does etc. > > > > > > The idea goes back a long way (!) and is in my 1995 > edition of Jongman > > > et al Data Analysis in Community and Landscape Ecology > (Cambridge > > > University Press) though most likely was in 1987 version > too. See > > > Section 5.4 of the Ordination chapter by Ter Braak in that > book. > > > > > > The idea is to find the direction (in the k-dimensional > ordination > > > space) that has maximal correlation with an external > variable. > > > > > > Hello, > > > > > > Then about Bray-Curtis. The referee may be correct when > writing that > > the fitted vectors are not directly related to Bray-Curtis. > You fit > > the vectors to the NMDS ordination, and that is a non-linear > mapping > > from Bray-Curtis to the metric ordination space. There are > two points > > here: non-linearity and stress. Because of these, it is not > strictly > > about B-C. Of course, the referee is wrong when writing > about NMDS > > axes: the fitted vector has nothing to do with axes (unless > you rotate > > your axis parallel to the fitted vector which you can do). > The NMDS is > > based on Bray-Curtis, but it is not the same, and the vector > fitting > > is based on NMDS. So why not write that is about NMDS? Why > to insist > > on Bray-Curtis which is only in the background? > > > Right, agreed. The analysis is one step removed from the B-C > but the > point of doing the nMDS was to find a low-d mapping of these > B-C > distances so in the sense that *if* the mapping is a good one > then we > can talk about correlations between "distances" between sites > and the > environmental variables. Whilst it might be strictly more > correct to > talk about this from the point of view of the nMDS the > implication is > that for significant envfit()s there is a significant linear > correlation > between the environmental variable(s) and the approximate > ranked > distances between samples. > > I mean, if all we talk about is the nMDS who cares? it is the > implications of this for the system under study that are of > interest. > > That said, B-C is just one of many ways to think of distance > so to my > mind I wouldn't even talk about the B-C distance either; the > interest is > in differences between sites/samples. The relevance of B-C or > some other > coefficient only comes in when considering if they are a good > descriptor > of the "distance" between samples for the variables you are > considering. > > Cheers, > > G > > > Cheers, Jari Oksanen > > > > -- > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~ > %~%~%~% > Dr. Gavin Simpson [t] +44 (0)20 7679 0522 > ECRC, UCL Geography, [f] +44 (0)20 7679 0565 > Pearson Building, [e] > gavin.simpsonATNOSPAMucl.ac.uk > Gower Street, London [w] > http://www.ucl.ac.uk/~ucfagls/ > UK. WC1E 6BT. [w] > http://www.freshwaters.org.uk > %~%~%~%~%~%~%~%~%~%
Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
Hi all, Im using envfit with some decomposition data currently but with a PCA result (via vegan:::rda()). Is envfit still valid for PCA results? I guess it doesnt make so very much difference, just the interpretation is slightly different. Or am I barking up the wrong tree by using this approach? Cheers, Alan -- Email: aghay...@gmail.com Mobile: +41794385586 Skype: aghaynes On 10 May 2012 12:53, Gavin Simpson wrote: > I've removed R-Help from this now... > > On Thu, 2012-05-10 at 10:13 +, Jari Oksanen wrote: > > On 10/05/2012, at 11:45 AM, Gavin Simpson wrote: > > > > As you provide little or no context I'll explain what envfit() does > etc. > > > > > > The idea goes back a long way (!) and is in my 1995 edition of Jongman > > > et al Data Analysis in Community and Landscape Ecology (Cambridge > > > University Press) though most likely was in 1987 version too. See > > > Section 5.4 of the Ordination chapter by Ter Braak in that book. > > > > > > The idea is to find the direction (in the k-dimensional ordination > > > space) that has maximal correlation with an external variable. > > > > > > Hello, > > > > > Then about Bray-Curtis. The referee may be correct when writing that > > the fitted vectors are not directly related to Bray-Curtis. You fit > > the vectors to the NMDS ordination, and that is a non-linear mapping > > from Bray-Curtis to the metric ordination space. There are two points > > here: non-linearity and stress. Because of these, it is not strictly > > about B-C. Of course, the referee is wrong when writing about NMDS > > axes: the fitted vector has nothing to do with axes (unless you rotate > > your axis parallel to the fitted vector which you can do). The NMDS is > > based on Bray-Curtis, but it is not the same, and the vector fitting > > is based on NMDS. So why not write that is about NMDS? Why to insist > > on Bray-Curtis which is only in the background? > > Right, agreed. The analysis is one step removed from the B-C but the > point of doing the nMDS was to find a low-d mapping of these B-C > distances so in the sense that *if* the mapping is a good one then we > can talk about correlations between "distances" between sites and the > environmental variables. Whilst it might be strictly more correct to > talk about this from the point of view of the nMDS the implication is > that for significant envfit()s there is a significant linear correlation > between the environmental variable(s) and the approximate ranked > distances between samples. > > I mean, if all we talk about is the nMDS who cares? it is the > implications of this for the system under study that are of interest. > > That said, B-C is just one of many ways to think of distance so to my > mind I wouldn't even talk about the B-C distance either; the interest is > in differences between sites/samples. The relevance of B-C or some other > coefficient only comes in when considering if they are a good descriptor > of the "distance" between samples for the variables you are considering. > > Cheers, > > G > > > Cheers, Jari Oksanen > > > > -- > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > Dr. Gavin Simpson [t] +44 (0)20 7679 0522 > ECRC, UCL Geography, [f] +44 (0)20 7679 0565 > Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk > Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ > UK. WC1E 6BT. [w] http://www.freshwaters.org.uk > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > > ___ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
I've removed R-Help from this now... On Thu, 2012-05-10 at 10:13 +, Jari Oksanen wrote: > On 10/05/2012, at 11:45 AM, Gavin Simpson wrote: > > As you provide little or no context I'll explain what envfit() does etc. > > > > The idea goes back a long way (!) and is in my 1995 edition of Jongman > > et al Data Analysis in Community and Landscape Ecology (Cambridge > > University Press) though most likely was in 1987 version too. See > > Section 5.4 of the Ordination chapter by Ter Braak in that book. > > > > The idea is to find the direction (in the k-dimensional ordination > > space) that has maximal correlation with an external variable. > > > Hello, > Then about Bray-Curtis. The referee may be correct when writing that > the fitted vectors are not directly related to Bray-Curtis. You fit > the vectors to the NMDS ordination, and that is a non-linear mapping > from Bray-Curtis to the metric ordination space. There are two points > here: non-linearity and stress. Because of these, it is not strictly > about B-C. Of course, the referee is wrong when writing about NMDS > axes: the fitted vector has nothing to do with axes (unless you rotate > your axis parallel to the fitted vector which you can do). The NMDS is > based on Bray-Curtis, but it is not the same, and the vector fitting > is based on NMDS. So why not write that is about NMDS? Why to insist > on Bray-Curtis which is only in the background? Right, agreed. The analysis is one step removed from the B-C but the point of doing the nMDS was to find a low-d mapping of these B-C distances so in the sense that *if* the mapping is a good one then we can talk about correlations between "distances" between sites and the environmental variables. Whilst it might be strictly more correct to talk about this from the point of view of the nMDS the implication is that for significant envfit()s there is a significant linear correlation between the environmental variable(s) and the approximate ranked distances between samples. I mean, if all we talk about is the nMDS who cares? it is the implications of this for the system under study that are of interest. That said, B-C is just one of many ways to think of distance so to my mind I wouldn't even talk about the B-C distance either; the interest is in differences between sites/samples. The relevance of B-C or some other coefficient only comes in when considering if they are a good descriptor of the "distance" between samples for the variables you are considering. Cheers, G > Cheers, Jari Oksanen > -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
On 10/05/2012, at 11:45 AM, Gavin Simpson wrote: > On Wed, 2012-05-09 at 15:51 -0600, Matt Bakker wrote: >> I'm getting lots of grief from reviewers about figures generated with >> the envfit function in the Vegan package. Has anyone else struggled to >> effectively explain this analysis? If so, can you share any helpful >> tips? >> >> The most recent comment I've gotten back: "What this shows is which >> NMDS axis separates the communities, not the relationship between the >> edaphic factor and the Bray-Curtis distance." > > Without further context for that quote and your manuscript to see how > you are using the method it is difficult to say whether you are doing > something silly or the reviewer is bone-headed. > > I've had similar comments from reviewers about my use of the ordisurf() > function. In each case it was the reviewers' failure to understand the > methods applied that was the cause of the confusion. > > As you provide little or no context I'll explain what envfit() does etc. > > The idea goes back a long way (!) and is in my 1995 edition of Jongman > et al Data Analysis in Community and Landscape Ecology (Cambridge > University Press) though most likely was in 1987 version too. See > Section 5.4 of the Ordination chapter by Ter Braak in that book. > > The idea is to find the direction (in the k-dimensional ordination > space) that has maximal correlation with an external variable. Hello, The method was indeed in the first edition of ter Braak's book. However, the idea is much older. The vegan implementation was based on an unpublished report from the Bell Labs from 1970s (or earlier). In this Bell Labs memorandum the method was specifically suggested for NMDS. Vegan uses different algorithm, but the method is the same. The early history in vegan can be traced in ORDNEWS correspondence from 2001 or so, but it is so old that I cannot find that message via this computer any longer. Then about Bray-Curtis. The referee may be correct when writing that the fitted vectors are not directly related to Bray-Curtis. You fit the vectors to the NMDS ordination, and that is a non-linear mapping from Bray-Curtis to the metric ordination space. There are two points here: non-linearity and stress. Because of these, it is not strictly about B-C. Of course, the referee is wrong when writing about NMDS axes: the fitted vector has nothing to do with axes (unless you rotate your axis parallel to the fitted vector which you can do). The NMDS is based on Bray-Curtis, but it is not the same, and the vector fitting is based on NMDS. So why not write that is about NMDS? Why to insist on Bray-Curtis which is only in the background? Cheers, Jari Oksanen -- Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers
On Wed, 2012-05-09 at 15:51 -0600, Matt Bakker wrote: > I'm getting lots of grief from reviewers about figures generated with > the envfit function in the Vegan package. Has anyone else struggled to > effectively explain this analysis? If so, can you share any helpful > tips? > > The most recent comment I've gotten back: "What this shows is which > NMDS axis separates the communities, not the relationship between the > edaphic factor and the Bray-Curtis distance." Without further context for that quote and your manuscript to see how you are using the method it is difficult to say whether you are doing something silly or the reviewer is bone-headed. I've had similar comments from reviewers about my use of the ordisurf() function. In each case it was the reviewers' failure to understand the methods applied that was the cause of the confusion. As you provide little or no context I'll explain what envfit() does etc. The idea goes back a long way (!) and is in my 1995 edition of Jongman et al Data Analysis in Community and Landscape Ecology (Cambridge University Press) though most likely was in 1987 version too. See Section 5.4 of the Ordination chapter by Ter Braak in that book. The idea is to find the direction (in the k-dimensional ordination space) that has maximal correlation with an external variable. Essentially, we have: E(z_j) = b_0 + b_1x_1 + b_2x_2 where E(z_j) is the expectation (or mean, or fitted values) of the jth external (environmental) variable, x_1 and x_2 are the "axis" scores in ordination dimensions 1 and 2, and b_y are unknown regression coefficients. This generalises to more than 2 dimensions or axes. The biplot arrow drawn goes from (0,0) to (b_1, b_2). You can see that the aim is to model or predict the values of the jth environmental variable (z_j) as a linear combination of the "axis" or site scores of the samples in the ordination space. Exactly the same idea underlies the ordisurf() function except that we use a GAM and for the right hand side of the equation multivariate splines are used which allow a non-linear surface instead of a plane. When applied to nMDS, if the nMDS provides a reasonable approximation to the original dissimilarities, then envfit() will estimate and show the strengths of the correlation and direction of maximal correlation between the nMDS configuration and the jth enviromental variable. This technique can be used to indicate if one or more environmental variables are associated with differences between sites/samples as represented in the nMDS ordination. The big caveat is the implication that the correlation or relationship between z_j and the ordination space is linear. ordisurf() allows you to relax this assumption as we fit a potentially non-linear surface to the ordination space instead of the plane that envfit() effectively produces (though we show only the direction of change with the arrow). So without seeing your manuscript or more context (and I'm not promising to read it or comment more if you provide it) I would suggest that, *if* you have applied nMDS and used envfit() correctly the combined analysis *does* reflect the *linear* "relationship between the edaphic factor and the Bray-Curtis distance", assuming of course that the nMDS has low stress (i.e fits the original dissimilarities well). In future, you should consider posting similar questions (ecological/environmental) to the R-SIG-Ecology list instead of the main R-Help list. I know Jari (lead developer of vegan and author of envfit() ) has stopped regularly reading the main R-Help list and you will get far more eyes familiar with these techniques on the R-SIG-Ecology list. I have taken the liberty of cc'ing this to the R-Sig-Ecology list so others can comment. HTH G > Thanks for any suggestions! > > > Matt > > __ > r-h...@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
