Dear list members!
I am trying to perform a PCNM analysis with function PCNM() (binary package
provided by the provided by project Spatial ecological data analysis with R). I
recently ran into a problem. I have a triangular distance matrix based on the
shortest distance between my sampling poin
On Wed, 2011-10-05 at 10:25 +0200, Emma Gothe wrote:
> Dear list members!
>
> I am trying to perform a PCNM analysis with function PCNM() (binary package
> provided by the provided by project Spatial ecological data analysis with R).
> I recently ran into a problem. I have a triangular distance
Thank you Jari for a fast reply!
I will try the vegan function asap as it seems to work smoothly. I am still
wondering what causes the problem in the PCNM () function though... If anyone
else knows, then please let me know.
Best Emma
-Ursprungligt meddelande-
Från: Jari Oksanen [mail
Hi All,
I'm trying to fit a theoretical distribution to some data, following the
procedure on Bolker's Ecological Models and Data in R, but I'm having some
problem... could someone please point me out what I'm doing wrong?
here is the code
#create a random variable
x <- sort(rpareto(100,1,0.
I am trying to calculate an AIC for my Fisher's log series (fisherfit). In
order to do this I need the log likelihood. Does anyone know how to
calculate the log likelihood for a specific alpha value. I know that the
second derivative of the log-likelihood is used to calculate the standard
error
Cory,
There is a profile method for fisherfit. If that's not enough, you can
have a look inside of fisherfit, and you'll see the Dev.logseries
internal function and how it is used in nlm to get estimate of alpha.
The same function can be used for manual profiling to get likelihood
values for a seq
On 5/10/11 20:42 PM, "Cory Redman" wrote:
> I am trying to calculate an AIC for my Fisher's log series (fisherfit). In
> order to do this I need the log likelihood. Does anyone know how to
> calculate the log likelihood for a specific alpha value. I know that the
> second derivative of the log
Dear Emma,
The main difference between PCNM::PCNM and vegan::pcnm functions is that
PCNM provides an immediate assessment of the spatial autocorrelation
(Moran's I) displayed by the computed eigenfunctions. The problem with your
data is that the computation of Moran's I is stopped, probably due to
