Dear list,
I am kind of new to R. I want to fit the nonlinear Morgan-Mercer-Flodin (MMF) growth model in a species accumulation curve (SAC) run with vegan package in .R. I would very much appreciate if you could give some feedback. My first question is what's the best approach i) grofit package or ii) nls2
i) This is what I am doing for grofit:
install.packages(grofit) # install grofit
library(grofit)
library(vegan)
library(nls2) #loads grofit, vegan and nls2 libraries
SAC.data<-read.table(file=file.choose(),header=TRUE,sep=",") # choose from directory
SAC.data # shows data
samples <- SAC.data$Samples #generates samples data
richness <- SAC.data$richness #generates richness data
TestRun <-grofit(samples, richness, TRUE) #runs the program for Gompertz function
##it's not working. I get this warning: Error in if ((dim(time)[1]) != (dim(data)[1])) stop("gcFit: Different number of datasets in data and time") :
argument is of length zero##
##Can't go further and add the MMF model##
ii) other approach:
SAC<-read.table(file=file.choose(),header=TRUE,sep=",") #choose from directory
UGE<-specaccum(SAC,method="exact",permutations=1000)
MMF.formula<-function(a1,b1,c1,d1,x){
+
(a1*b1+c1*x^d1)/(b1+x^d1)
+
} ## shorthand for creating model formula
MMF.formula # shows formula
MMF.fit<-nls(richness~MMF.formula(a,b,c,d,samples),data=UGE,start=list(a=-2,b=3,c=50,d=0.5),trace=TRUE) # MMF fitted model
##not working. Get this message: Error in nls(richness ~ MMF.formula(a, b, c, d, samples), data = SAC.data, :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562## starting values were just guesses##
any help would be appreciated
cheers
Jo?o
Jo?o Canning Clode, Ph.D
Postdoctoral Fellow
Marine Invasions Research Lab
Smithsonian Environmental Research Center
647 Contees Wharf Road
Edgewater, MD 21037
Email: canning-clodej at si.edu<mailto:canning-clodej at si.edu>
Web: www.canning-clode.com<http://www.canning-clode.com/>
On Jun 9, 2010, at 2:19 PM, Falk Hildebrand wrote:
Dear list,
I have been using the vegan package to do mds via the metaMDS function, but I have some questions regarding the output.
1) First off about the rankindex function {vegan}: On my data I always get values that I would consider as low, e.g. something in the range of 0.0344 as best result (euclidean) and the mean being 0.028 over 7 other metrices. Do results as low as this have any relevance? Are there some guidelines as to what absolute (or relative) values one should at least obtain to make a distinction?
2) Is there a way to estimate what percentage of the variation within the data can be explained by the mds?
3) using envfit {vegan} I get significant p-values for 5 out of 14 env. variables/factors (which is of course very nice). However, if I do a CCA and a ANOVA (call: anova(cca,by="terms",permu=200)) with the same environmental values, usually only one of these same variables/factors ends up being significant. I am aware that these are different techniques, but I always thought that CCA was supposed to "force" the ordination on the env. vars, so why then would I get much better p-values for the unconstrained nmds (I use 5 dimensions in the nmds)?
4) how can I interpret the relation between species and the environmental fit in a nmds plot call? The same as sites and env. fit?
e.g.
ef=envfit(nmds,environment)
plot(ef); points(nmds, dis = "species");
Any help or links to relevant literature would be greatly appreciated.
best,
Falk
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