labdsv (version 2.1-0)

predict: Predict species abundances in an ordination

Description

This function fits a Generalized Additive Model (GAM) for each species in a data.frame against an ordination.

Usage

# S3 method for dsvord
predict(object,comm,minocc=5,dims=1:ncol(object$points),
                         family='nb',gamma=1,keep.models=FALSE,...)

Value

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.

Arguments

object

an object of class dsvord

comm

a community matrix or data.frame with samples as rows and species as columns

minocc

the minimum number of occurrences to model a species

dims

which specific dimensions to include

family

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

gamma

the gamma parameter to control fitting GAM models

keep.models

a switch to control saving the individual GAM models

...

ancillary arguments to function predict

Author

David W. Roberts

Details

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.

See Also

calibrate for the complementary function that fits GAM models for environment variables

Examples

data(bryceveg)
dis.man <- dist(bryceveg,method="manhattan")
demo.nmds <- nmds(dis.man,k=4)
if (FALSE) res <- predict(demo.nmds,bryceveg,minocc=10)