[R-sig-eco] a possible hack for adding species scores?

[Dave Roberts]

Hi Eliot,

    With respect to leafiness you're in a common problem area.  Ordinal 
variables are hard to handle from any approach.  One alternative (only 
undertaken with a well articulated rationale) is to promote them to 
interval variables,

ordisurf(demo, as.numeric(to.ordinate$leafiness))

If you think the scale is wrong you can try other, e.g.

0,1,4,9,16,25

Otherwise you can do logistic regression on the classes

ordisurf(demo, to.ordinate$leafiness==1, family=binomial)

but you have one fewer degree of freedom than expected which can be a 
problem on a small data set.  Alternatively, you can use envfit as you 
have been doing, but that also assumes they are categorical as opposed 
to ordered.

    Maybe someone else will weigh in with a better alternative for 
ordinal variables.

Dave


On 08/29/2014 11:05 AM, Eliot Miller wrote:
> Hi Dave,
>
> Thanks a million for the response! Your solution is good in some
> respects--when applied to my real data it shows me why I should be cautious
> of assuming any linearity in the response of my continuous variables.
> However, it doesn't work for the factors (which makes sense). You can run,
>
> ordisurf(demo, to.ordinate$leafiness)
>
> to see what I mean. Would it be reasonable to use some combination of
> ordisurf for the continuous variables, with the locations of the factors
> determined via envfit?
>
> Thanks again for the push in the right direction!
>
> Cheers,
> Eliot
>
>
> On Wed, Aug 27, 2014 at 10:24 AM, Eliot Miller<eliotmiller at umsl.edu>  wrote:
>
>> Hi all,
>>
>> A few months ago I ran into a problem that others seem to have had. In
>> short, if you use the FD package to calculate a Gower distance matrix which
>> you then either feed back into vegan or use directly in FD, you "lose the
>> species" scores associated with the points. You can't see what explains the
>> distances among points. Examples of similar problems are here:
>>
>> http://r.789695.n4.nabble.com/Ordination-Plotting-Warning-Species-scores-not-available-td4664025.html
>> https://www.mail-archive.com/r-help@r-project.org/msg153765.html
>>
>> I am less concerned with the plotting of the results than the previous
>> posters have been. I am more concerned with simply seeing the table of
>> "loadings" (don't think that term is correct for the PCoA). Perhaps this is
>> an acceptable and widespread solution to the problem, and I simply haven't
>> stumbled on it yet. It takes advantage of the vegan function envfit(). Or
>> perhaps it is an incorrect solution. I'm wondering which of those two
>> things it is, and hoping for a bit of input. In my case, I am making a
>> distance matrix among foraging observations spread across a number of
>> species. I want to know each species' niche size using the FDis metric. I
>> made an example dataset and completely worked through example. The main
>> point here is that I want to know whether the object "fitted" at the end is
>> correctly explaining how things are "loaded". Any help is greatly
>> appreciated.
>>
>> Thanks in advance!
>> Eliot
>>
>> library(FD)
>>
>> #these first few lines are just building up an example dataset of foraging
>> observations
>> set.seed(0)
>> maneuver<-
>> sample(c(rep("sally",10),rep("probe",10),rep("glean",10),rep("hover",10)))
>>
>> set.seed(0)
>> substrate<-
>> sample(c(rep("leaf",10),rep("flower",10),rep("air",10),rep("branch",10)))
>>
>> set.seed(0)
>> attack.height<- rnorm(40, mean=5)
>>
>> set.seed(0)
>> relative.height<- rnorm(40, 0.5)
>> relative.height[relative.height<  0]<- 0
>> relative.height[relative.height>  1.1]<- 1.1
>>
>> set.seed(0)
>> leafiness<- sample(c(0:5), size=40, replace=TRUE)
>> #convert this to an ordinal variable
>> leafiness<- ordered(leafiness)
>>
>> set.seed(0)
>> distance<- sample(c(1:4), size=40, replace=TRUE)
>> #convert to ordinal
>> distance<- ordered(distance)
>>
>> #here is the complete dataset of example foraging observations
>> to.ordinate<- data.frame(maneuver, substrate, attack.height,
>> relative.height, leafiness, distance)
>>
>> #now make a second table that identifies to which species each observation
>> belongs
>> road.map<- matrix(ncol=40, nrow=3, 0)
>>
>> colnames(road.map)<- 1:40
>> row.names(road.map)<- c("species1","species2","species3")
>>
>> #this for loop assigns each foraging observations to one of the species
>> set.seed(0)
>> for(i in 1:dim(road.map)[2])
>> {
>>      species<- sample(row.names(road.map), 1)
>>      road.map[species,i]<- 1
>> }
>>
>> #run the FD calculations
>> FDresults<- dbFD(x=to.ordinate, a=road.map, corr="lingoes",
>> calc.FRic=FALSE, calc.FGR=FALSE, calc.CWM=FALSE, calc.FDiv=FALSE,
>> print.pco=TRUE)
>>
>> #use the vegan function envfit() to see what is driving each axis from the
>> PCoA
>> fitted<- envfit(ord=FDresults$x.axes, env=to.ordinate, permutations=0,
>> choices=1:10)
>>
>>
>
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-- 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
David W. Roberts                                     office 406-994-4548
Professor and Head                                      FAX 406-994-3190
Department of Ecology                         email droberts at montana.edu
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