gdm-package {gdm}

Overview of the functions in the gdm package

Description

Generalized Dissimilarity Modeling is a statistical technique for modelling variation in biodiversity between pairs of geographical locations or through time. The gdm package provides functions to fit, evaluate, summarize, and plot Generalized Dissimilarity Models and to make predictions (across space and/or through time) and map biological patterns by transforming environmental predictor variables.

Details

The functions in the gdm package provide the tools necessary for fitting GDMs, including functions to prepare biodiversity and environmental data. Major functionality includes:

• Formatting various types of biodiversity and environmental data to gdm's site-pair format used in model fitting

• Fitting GDMs using geographic and environmental distances between sites

• Plotting fitted functions & extracting I-spline values

• Estimating predictor importance using matrix permutation and predictor contributions using deviance paritioning

• Using cross-validation to evaluate models

• Predicting pairwise dissimiliarites between sites or times and transforming environmental predictors to biological importance and mapping these patterns.

To see the preferable citation of the package, type citation("gdm").

I. Formatting input data

GDM fits biological distances to pairwise site geographical and environmental distances. Most users will need to first format their data to gdm's site-pair table format:

formatsitepair	To convert biodiversity and environmental data to site-pair format

II. Model fitting, evaluation, and summary

gdm	To fit a GDM
gdm.crossvalidation	To evaluate a GDM
gdm.partition.deviance	To asses predictor contributions to deviance explained
gdm.varImp	To asses model significance and predictor importance
summary	To summarize a GDM

III. Model prediction and transformation of environmental data

predict	To predict biological dissimilarities between sites in space or between time periods
gdm.transform	To transform each environmental predictor to biological importance

IV. Plotting model output and fitted functions

plot	To plot model fit and I-splines
isplineExtract	To extract I-spline values to allow for custom plotting
plotUncertainty	To estimate and plot model sensitivity using bootstrapping

Author(s)

The gdm development team is Matt Fitzpatrick and Karel Mokany. The R package is based on code originally developed by Glenn Manion under the direction of Simon Ferrier. Where others have contributed to individual functions, credits are provided in function help pages.

The maintainer of the R version of gdm is Matt Fitzpatrick <[e-mail]>.