View source: R/randPointsRespectingSelfOther2.r
randPointsRespectingSelfOther2 | R Documentation |
This function randomizes the location of two sets of geographic points with respect to one another retaining (more or less) the same distribution of pairwise distances between points with and between sets (plus or minus a user-defined tolerance).
randPointsRespectingSelfOther2( x1, x2, rast, tol1 = NULL, tol2 = NULL, tol12 = NULL, distFunct = NULL, restrict = TRUE, keepData = FALSE, verbose = FALSE, ... )
x1 |
Matrix, data frame, SpatialPoints, or SpatialPointsDataFrame object. If this is a matrix or data frame, the first two columns must represent longitude and latitude (in that order). If |
x2 |
As |
rast |
Raster, RasterStack, or RasterBrick used to locate presences randomly. If this is a RasterStack or a RasterBrick then the first layer will be used (i.e., so cells with |
tol1 |
Numeric >0, maximum root-mean-square distance allowed between the set of observed pairwise distances between points in |
tol2 |
As |
tol12 |
As |
distFunct |
Either a function or |
restrict |
Logical, if |
keepData |
Logical, if |
verbose |
Logical, if |
... |
Arguments to pass to |
A list with two elements, each representing object of the same classes as x1
and x2
but with coordinates randomized.
randPointsRespectingSelf
, randPointsRespectingSelfOther1
, randPointsBatch
, randPointsBatchSampled
, randPointsBatchExtract
, randPointsBatchNicheOverlap
library(dismo) library(raster) data(lemurs, package='enmSdm') longLat <- c('decimalLongitude', 'decimalLatitude') mad <- raster::getData('GADM', country='MDG', level=0) elev <- raster::getData('alt', country='MDG', mask=TRUE, res=2.5) # plot data as-is plot(mad) species <- sort(unique(lemurs$species)) for (i in seq_along(species)) { thisLemur <- lemurs[lemurs$species == species[i], longLat] points(thisLemur, pch=i, col=i) } legend('bottomleft', legend=species, pch=seq_along(species), col=seq_along(species)) # geographically thin presences of each species thinLemurs <- data.frame() for (i in seq_along(species)) { thisLemur <- lemurs[lemurs$species == species[i], ] thinned <- geoThin(thisLemur, minDist=10000, longLat=longLat) thinLemurs <- rbind(thinLemurs, thinned) } # plot geographically thinned data plot(mad) for (i in seq_along(species)) { thisLemur <- thinLemurs[thinLemurs$species == species[i], longLat] points(thisLemur, pch=i, col=i) } legend('bottomleft', legend=species, pch=seq_along(species), col=seq_along(species)) # randomize one species with respect to itself x <- thinLemurs[thinLemurs$species == 'Eulemur fulvus', longLat] set.seed(123) x1rand <- randPointsRespectingSelf(x=x, rast=elev, tol=24000, verbose=TRUE) # plot observed and randomized occurrences plot(mad) points(x, pch=16) points(x1rand, col='red') # randomize two species with respect to selves and others species1 <- species[1] species2 <- species[3] x1 <- thinLemurs[thinLemurs$species == species1, longLat] x2 <- thinLemurs[thinLemurs$species == species2, longLat] set.seed(123) tol1 <- tol2 <- tol12 <- 16000 x12rand <- randPointsRespectingSelfOther2(x1=x1, x2=x2, rast=elev, tol1=tol1, tol2=tol2, tol12=tol12, verbose=TRUE) # plot geographically thinned data plot(mad) points(x1, pch=21, bg='cornflowerblue') points(x2, pch=24, bg='cornflowerblue') points(x12rand$x1rand, pch=1, col='red') points(x12rand$x2rand, pch=2, col='red') legend('bottomleft', legend=c(species1, species2, legend=paste('rand', species1), paste('rand', species2)), pch=c(21, 24, 1, 2), col=c('black', 'black', 'red', 'red'), pt.bg=c('cornflowerblue', 'cornflowerblue', NA, NA)) ### batch mode ## Not run: # download climate data clim <- raster::getData('worldclim', var='bio', res=2.5) # lemur data data(lemurs, package='enmSdm') longLat <- c('decimalLongitude', 'decimalLatitude') # geographically thin presences of each species thinLemurs <- data.frame() for (i in seq_along(species)) { thisLemur <- lemurs[lemurs$species == species[i], ] thinned <- geoThin(thisLemur, minDist=10000, longLat=longLat) thinLemurs <- rbind(thinLemurs, thinned) } # randomize two species with respect to selves and others species1 <- species[1] species2 <- species[3] x1 <- thinLemurs[thinLemurs$species == species1, longLat] x2 <- thinLemurs[thinLemurs$species == species2, longLat] # create null distributions set.seed(123) tol1 <- tol2 <- tol12 <- 24000 iterations <- 100 # for analysis set this to 100 or more # for testing use a small number! x12rand <- randPointsBatch('randPointsRespectingSelfOther2', x1=x1, x2=x2, rast=clim[[1]], tol1=tol1, tol2=tol2, tol12=tol12, iterations=iterations, verbose=TRUE) # get environment that was sampled to use as background bg <- randPointsBatchSampled(x12rand) bgEnv <- raster::extract(clim, bg) # create PCA of environmental space vars <- paste0('bio', 1:19) bgPca <- princomp(bgEnv[ , vars], cor=TRUE) x1env <- raster::extract(clim, x1) x2env <- raster::extract(clim, x2) nas1 <- omnibus::naRows(x1env) nas2 <- omnibus::naRows(x2env) if (length(nas1) > 0) x1env <- x1env[-nas1, ] if (length(nas2) > 0) x2env <- x2env[-nas2, ] # observed niche overlap obsOverlap <- enmSdm::nicheOverlap( x1=x1env, x2=x2env, env=bgPca, vars=vars, bins=100, cor=TRUE ) # extract climate at randomized sites x12rand <- randPointsBatchExtract(x12rand, clim, verbose=TRUE) # null niche overlap nullOverlap <- randPointsBatchNicheOverlap( rands=x12rand, env=bgPca, vars=vars, bins=100, cor=TRUE ) hist(nullOverlap$d, 20, main='Niche Overlap', xlab='Schoener\'s D', xlim=c(0, 1)) abline(v=obsOverlap[['d']], col='blue', lwd=3) legend('topright', legend='Observed', lwd=3, col='blue') ## End(Not run)
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