| predict.traitglm {mvabund} | R Documentation |
Predictions from fourth corner model fits
Description
Obtains a prediction from a fitted fourth corner model object.
Usage
## S3 method for class 'traitglm'
predict(object, newR=NULL, newQ=NULL, newL=NULL, type="response", ...)
Arguments
object |
a fitted object of class |
newR |
A new data frame of environmental variables. If omitted, the original matrix of environmental variables is used. |
newQ |
A new data frame of traits for each response taxon. If omitted, the original matrix of traits is used. |
newL |
A new data frame of abundances (sites in rows, taxa in columns). This is only used if seeking predicted log-likelihoods. If omitted, the original abundances are used. |
type |
The type of prediction required. The default is predictions on the scale of the response variable, alternatives are |
... |
Further arguments passed to or from other methods. |
Details
If newR and newQ are omitted, then as usual, predictions are based on the data used for the fit. Note that two types of predictions are possible in principle: predicting at new sites (by specifying a new set of environmental variables only, as newR) and predicting for new taxa (by specifying a new set of traits only, as newQ). Unfortunately, only predicting at new sites has been implemented at the moment! An issue with predicting to new taxa is that a main effect is included in the model for each taxon (by default), and the intercept would be unknown for a new species.
If predictive log-likelihoods are desired, a new data frame of abundances newL would need to be specified, whose rows correspond to those of newR and whose columns correspond to rows of newQ.
Value
A matrix of predictions, with sites in rows and taxa in columns.
Author(s)
David I. Warton <David.Warton@unsw.edu.au>
References
Brown AM, Warton DI, Andrew NR, Binns M, Cassis G and Gibb H (2014) The fourth corner solution - using species traits to better understand how species traits interact with their environment, Methods in Ecology and Evolution 5, 344-352.
See Also
Examples
data(antTraits)
# fit a fourth corner model using negative binomial regression via manyglm:
ft=traitglm(antTraits$abund,antTraits$env,antTraits$traits,method="manyglm")
ft$fourth #print fourth corner terms
# predict to the first five sites
predict(ft, newR=antTraits$env[1:5,])