predict: Predict species abundances in an ordination

In labdsv: Ordination and Multivariate Analysis for Ecology

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

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

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.

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.

Author(s)

David W. Roberts [e-mail]

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)
## Not run: res <- predict(demo.nmds,bryceveg,minocc=10)

labdsv documentation built on April 10, 2023, 5:08 p.m.