# Predict Biological Dissimilarities Between Sites or Times Using a Fitted Generalized Dissimilarity Model This function predicts biological distances between sites or times using a model object returned from `gdm`. Predictions between site pairs require a data frame containing the values of predictors for pairs of locations, formatted as follows: distance, weights, s1.X, s1.Y, s2.X, s2.Y, s1.Pred1, s1.Pred2, ..., s1.PredN, s2.Pred1, s2.Pred2, ..., s2.PredN, ..., Predictions of biological change through time require two raster stacks or bricks for environmental conditions at two time periods, each with a layer for each environmental predictor in the fitted model. ```r ## S3 method for class 'gdm' predict(object, data, time=FALSE, predRasts=NULL, filename="", ...) ``` ## Arguments - `object`: A gdm model object resulting from a call to `gdm`. - `data`: Either a data frame containing the values of predictors for pairs of sites, in the same format and structure as used to fit the model using `gdm` or a raster stack if a prediction of biological change through time is needed. For a data frame, the first two columns - distance and weights - are required by the function but are not used in the prediction and can therefore be filled with dummy data (e.g. all zeros). If geo is TRUE, then the s1.X, s1.Y and s2.X, s2.Y columns will be used for calculating the geographical distance between each site for inclusion of the geographic predictor term into the GDM model. If geo is FALSE, then the s1.X, s1.Y, s2.X and s2.Y data columns are ignored. However these columns are still REQUIRED and can be filled with dummy data (e.g. all zeroes). The remaining columns are for N predictors for Site 1 and followed by N predictors for Site 2. The order of the columns must match those in the site-pair table used to fit the model. A raster stack should be provided only when time=T and should contain one layer for each environmental predictor in the same order as the columns in the site-pair table used to fit the model. - `time`: TRUE/FALSE: Is the model prediction for biological change through time? - `predRasts`: A raster stack characterizing environmental conditions for a different time in the past or future, with the same extent, resolution, and layer order as the data object. Required only if time=T. - `filename`: character. Output filename for rasters. When provided the raster layers are written to file directly. - `...`: additional arguments to pass to terra `predict` function. ## Returns predict returns either a response vector with the same length as the number of rows in the input data frame or a raster depicting change through time across the study region. ## Examples ```r ##set up site-pair table using the southwest data set sppData <- southwest[, c(1,2,14,13)] envTab <- southwest[, c(2:ncol(southwest))] # remove soils (no rasters for these) envTab <- envTab[,-c(2:6)] sitePairTab <- formatsitepair(sppData, 2, XColumn="Long", YColumn="Lat", sppColumn="species", siteColumn="site", predData=envTab) # create GDM gdmMod <- gdm(sitePairTab, geo=TRUE) ##predict GDM predDiss <- predict(gdmMod, sitePairTab) ##time example rastFile <- system.file("./extdata/swBioclims.grd", package="gdm") envRast <- terra::rast(rastFile) ##make some fake climate change data futRasts <- envRast ##reduce winter precipitation by 25% futRasts[[3]] <- futRasts[[3]]*0.75 timePred <- predict(gdmMod, envRast, time=TRUE, predRasts=futRasts) terra::plot(timePred) ``` ## See Also `gdm.transform`

predict.gdm function