This function fits a Generalized Additive Model (GAM) for each species in a data.frame against an ordination.
# S3 method for dsvord
predict(object,comm,minocc=5,dims=1:ncol(object$points),
family='nb',gamma=1,keep.models=FALSE,...)A list object with vector elements aic, dev.expl, adj.rsq, and matrix fitted. Optionally, if keep.models is TRUE, a list with all of the GAM models fitted. list element aic gives the model AICs for each species, dev.expl gives the deviance explained, adj.rsq gives the adjusted r-Squared, and fitted gives the expected abundance of each species in each sample unit.
an object of class dsvord
a community matrix or data.frame with samples as rows and species as columns
the minimum number of occurrences to model a species
which specific dimensions to include
the error distribution specifier for the GAM function; can be 'nb' for negative binomial, 'poisson' for the Poisson distribution, or 'binomial' for presence/absence data
the gamma parameter to control fitting GAM models
a switch to control saving the individual GAM models
ancillary arguments to function predict
David W. Roberts droberts@montana.edu http://ecology.msu.montana.edu/droberts/droberts.html
The predict function sequentially and independently fits a GAM model of each species distribution as a function of ordination coordinates, using the family and gamma specifiers supplied in the function call, or their defaults. The function fits two or three dimensional models; if the length of dims is greater than three the dimensions are truncated to the first three chosen.
calibrate for the complementary function that fits GAM models for environment variables
data(bryceveg)
dis.man <- dist(bryceveg,method="manhattan")
demo.nmds <- nmds(dis.man,k=4)
if (FALSE) res <- predict(demo.nmds,bryceveg,minocc=10)
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