View source: R/gdm.transform.R
gdm.transform | R Documentation |
This function transforms geographic and environmental predictors using (1) the
fitted functions from a model object returned from gdm
and (2) a
data frame or raster stack containing predictor data for a set of sites.
gdm.transform(model, data)
model |
A gdm model object resulting from a call to |
data |
Either (i) a data frame containing values for each predictor variable in the model, formatted as follows: X, Y, var1, var2, var3, ..., varN or (ii) a raster stack with one layer per predictor variable used in the model, excluding X and Y (rasters for x- and y-coordinates are built automatically from the input rasters if the model was fit with geo=T). The order of the columns (data frame) or raster layers (raster stack) MUST be the same as the order of the predictors in the site-pair table used in model fitting. There is currently no checking to ensure that the order of the variables to be transformed are the same as those in the site-pair table used in model fitting. If geographic distance was not used as a predictor in model fitting, the x- and y-columns need to be removed from the data to be transformed. Output is provided in the same format as the input data. |
gdm.transform returns either a data frame with the same number of rows as the input data frame or a raster stack, depending on the format of the input data. If the model uses geographic distance as a predictor the output object will contain columns or layers for the transformed X and Y values for each site. The transformed environmental data will be in the remaining columns or layers.
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, 252-264.
Fitzpatrick MC, Keller SR (2015) Ecological genomics meets community-level modeling of biodiversity: Mapping the genomic landscape of current and future environmental adaptation. Ecology Letters 18: 1-16
# start with the southwest data set # grab the columns with xy, site ID, and species data sppTab <- southwest[, c("species", "site", "Lat", "Long")] ##fit gdm using rasters rastFile <- system.file("./extdata/swBioclims.grd", package="gdm") envRast <- raster::stack(rastFile) sitePairRast <- formatsitepair(sppTab, 2, XColumn="Long", YColumn="Lat", sppColumn="species", siteColumn="site", predData=envRast) ##remove NA values sitePairRast <- na.omit(sitePairRast) ##fit raster GDM gdmRastMod <- gdm(sitePairRast, geo=TRUE) ##raster input, raster output transRasts <- gdm.transform(gdmRastMod, envRast) # map biological patterns rastDat <- raster::sampleRandom(transRasts, 10000) pcaSamp <- prcomp(rastDat) # note the use of the 'index' argument pcaRast <- raster::predict(transRasts, pcaSamp, index=1:3) # scale rasters pcaRast[[1]] <- (pcaRast[[1]]-pcaRast[[1]]@data@min) / (pcaRast[[1]]@data@max-pcaRast[[1]]@data@min)*255 pcaRast[[2]] <- (pcaRast[[2]]-pcaRast[[2]]@data@min) / (pcaRast[[2]]@data@max-pcaRast[[2]]@data@min)*255 pcaRast[[3]] <- (pcaRast[[3]]-pcaRast[[3]]@data@min) / (pcaRast[[3]]@data@max-pcaRast[[3]]@data@min)*255 raster::plotRGB(pcaRast, r=1, g=2, b=3)
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