NegBin() and ZINB() incorrectly specified the gamma part of the
distribution. The shape argument to rgamma() should have been
1/alpha where alpha was used previously.
Also clarified the paramterization of the negative binomial used
by NegBin() and ZINB as the NB2 version.
NegBin() and ZINB() allow for vector alpha inputs. #25
R CMD check in the
development version of R.Jari Oksanen is now listed as a contributor to the package having added several new stochastic distributions.
The object returned by coenocline() now has S3 class "coenocline"
and inherits from the "matrix" class.
A print() method has been added for coenocline() which displays
some summary information and the first n lines of the simulated
counts. The print() method uses a new internal function modelled
on the way dplyr prints data frames.
A stack() method for coenocline() was added. This makes it much
easier to reshape the simulated count data into a format suitable for
use with ggplot or lattice graphics, or R's modelling
functions.
An enhanced plot() method for coenocline() objects is provided,
which can draw 1-d plots of single gradient simulations.
A persp() method is now provided which can produced 3-d perspective
plots od simulations with 2 gradients.
Two new stochastic distributions were added by Jari Oksanen
A new extractor function is provided, locations(), which extracts
the gradient locations at which counts were simulated.
gamma parameter for the second gradient
was being ignored, and the value of gamma for the first gradient
was used instead.An R package for coenocline simulation; generating simulated species abundance or occurence data along one or two gradients
First public release of coenocliner on CRAN
Species response can be parameterised using either the classic Gaussian response model or the generalise beta response model
Random count or occurence data can be simulated from species responses using random draws from a Poisson, Negative Binomial, Binomial, Beta-binomial, ZIP, ZINB, or Bernoulli distribution with the parameterised response curve taken as the mean or expectation of the distribution to draw from
The main user-facing function is coenocline(). See ?coenocliner
and ?coenocline for further details and examples of usage
A basic overview and introductory tutorial for coenocliner is available.
Run browseVignettes("coenocliner") in R to access the PDF, R code and
sources.