Description Usage Arguments Value References See Also Examples
View source: R/GDM_Table_Funcs.R
For an overview of the functions in the gdm package have a look here: gdmpackage
.
The gdm function is used to fit a generalized dissimilarity model to tabular sitepair data formatted as follows using the formatsitepair
function: distance, weights, s1.xCoord, s1.yCoord, s2.xCoord, s2.yCoord, s1.Pred1, s1.Pred2, ..., s1.PredN, s2.Pred1, s2.Pred2, ..., s2.PredN. The first column (Response) must be any ratio based dissimilarity (distance) measure between Site 1 and Site 2. The second column defines any weighting to be applied during fitting of the model. If equal weighting is required, then all entries in this column should be set to 1.0 (default). The third and fourth columns, s1.X and s1.Y, represent the spatial coordinates of the first site in the site pair (s1). The fifth and sixth columns, s2.X and s2.Y, represent the coordinates of the second site (s2). Note that the first six columns are REQUIRED, even if you do not intend to use geographic distance as a predictor (in which case these columns can be loaded with dummy data if the actual coordinates are unknown  though that would be weird, no?). The next N*2 columns contain values for N predictors for Site 1, followed by values for the same N predictors for Site 2.
The following is an example of a GDM input table header with three environmental predictors (Temp, Rain, Bedrock):
distance, weights, s1.xCoord, s1.yCoord, s2.xCoord, s2.yCoord, s1.Temp, s1.Rain, s1.Bedrock, s2.Temp, s2.Rain, s2.Bedrock
1 
data 
A data frame containing the site pairs to be used to fit the GDM (typically obtained using the 
geo 
Set to TRUE if geographic distance between sites is to be included as a model term. Set to FALSE if geographic distance is to be omitted from the model. Default is FALSE. 
splines 
An optional vector of the number of Ispline basis functions to be used for each predictor in fitting the model. If supplied, it must have the same length as the number of predictors (including geographic distance if geo is TRUE). If this vector is not provided (splines=NULL), then a default of 3 basis functions is used for all predictors. 
knots 
An optional vector of knots in units of the predictor variables to be used in the fitting process. If knots are supplied and splines=NULL, then the knots argument must have the same length as the number of predictors * 3. If both knots and the number of splines are supplied, then the length of the knots argument must be the same as the sum of the values in the splines vector. Note that the default values for knots when the default three Ispline basis functions are 0 (minimum), 50 (median), and 100 (maximum) quantiles. 
gdm returns a gdm model object. The function summary.gdm
can be used to obtain or print a synopsis of the results. A gdm model object is a list containing at least the following components:


dataname 
The name of the table used as the data argument to the model. 
geo 
Whether geographic distance was used as a predictor in the model. 
gdmdeviance 
The deviance of the fitted GDM model. 
nulldeviance 
The deviance of the null model. 
explained 
The percentage of null deviance explained by the fitted GDM model. 
intercept 
The fitted value for the intercept term in the model. 
predictors 
A list of the names of the predictors that were used to fit the model. 
coefficients 
A list of the coefficients for each spline for each of the predictors considered in model fitting. 
knots 
A vector of the knots derived from the x data (or user defined), for each predictor. 
splines 
A vector of the number of Ispline basis functions used for each predictor. 
creationdate 
The date and time of model creation. 
observed 
The observed response for each site pair (from data column 1). 
predicted 
The predicted response for each site pair, from the fitted model (after applying the link function). 
ecological 
The linear predictor (ecological distance) for each site pair, from the fitted model (before applying the link function). 
Ferrier S, Manion G, Elith J, Richardson, K (2007) Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity & Distributions 13, 252264.
formatsitepair, summary.gdm,
plot.gdm, predict.gdm, gdm.transform
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ##fit table environmental data
##sets up sitepair table, environmental tabular data
load(system.file("./data/gdm.RData", package="gdm"))
sppData < gdmExpData[c(1,2,13,14)]
envTab < gdmExpData[c(2:ncol(gdmExpData))]
sitePairTab < formatsitepair(sppData, 2, XColumn="Long", YColumn="Lat", sppColumn="species",
siteColumn="site", predData=envTab)
##fit table GDM
gdmTabMod < gdm(sitePairTab, geo=TRUE)
summary(gdmTabMod)
##fit raster environmental data
##sets up sitepair table
rastFile < system.file("./extdata/stackedVars.grd", package="gdm")
envRast < stack(rastFile)
##environmental raster data
sitePairRast < formatsitepair(sppData, 2, XColumn="Long", YColumn="Lat", sppColumn="species",
siteColumn="site", predData=envRast)
##sometimes raster data returns NA in the sitepair table, these rows will have to be removed
##before fitting gdm
sitePairRast < na.omit(sitePairRast)
##fit raster GDM
gdmRastMod < gdm(sitePairRast, geo=TRUE)
summary(gdmRastMod)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.