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

[Eliot Miller]

Hi Dave,

Apologies for the slow response. The addition you provided in your second
response works great. I really appreciate the input! Now to figure out what
my real results mean.

Thanks!

Eliot


On Fri, Aug 29, 2014 at 12:05 PM, Eliot Miller <eliotmiller at umsl.edu> 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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