heat_up: Convert log-likelihoods of any vegdistmod PDF to a raster...

In rsh249/vegdistmod: Vegetation distribution modeling and spatial informatics

Description

This is not an ENM. What it does do is provides a geographic representation of the likelihood functions that are used in CRACLE and the spatial defragmentation functions.

Usage

heat_up(clim, dens, parallel = FALSE, nclus = 4, type = ".kde",
  w = FALSE)

Arguments

clim	A raster object of climate data (matching ext_ob)
dens	A vegdistmod density object (see densform())
parallel	Make use of multicore architecture
nclus	If parallel is TRUE, how many cores should be allocated.
type	Which PDF should be used, .gauss or .kde
w	TRUE or FALSE should variable PDFs be weighted by relative niche breadth.

Author(s)

Robert Harbert, [e-mail]

Avery Hill

Examples

## Not run: 
data(abies);
data(climondbioclim);
ext.abies = extraction(abies, climondbioclim, schema='raw', rm.outlier=TRUE, alpha = 0.005);
dens <- densform(ext.abies, climondbioclim, manip = 'condi', 
                 kern = 'gaussian', n = 128, bg.n = 1000)
h = heat_up(climondbioclim, dens, parallel=FALSE, type = '.kde', nclus =4)
hs = sum(h)
ex.h = raster::extract(hs, ext.abies[,4:3])
plot(hs>sort(ex.h)[ceiling(0.01*length(ex.h))])
points(ext.abies[,4:3], col ='green')
 
##Bootstrap: With train/test subsetting and model evaluation

binary = list();
bin.auc = list();
ev.auc = list();
data.ex = extraction(abies, climondbioclim, schema='flat', factor =2, rm.outlier=TRUE, alpha = 0.01)
for (i in 1:100){
pick = as.numeric(sample(data.ex[,1], 0.5*length(data.ex[,1]), replace =F));
train = data.ex[which(data.ex[,1] %in% pick),]
test = data.ex[-which(data.ex[,1] %in% pick),]
d.train <- densform(train, climondbioclim, 
     manip = 'condi', kern = 'gaussian', n = 128, bg.n = 1000)
h.t = heat_up(climondbioclim, d.train, parallel=TRUE, type = '.kde', nclus =4)
hs.t = sum(h.t)
ex.h = raster::extract(hs.t, test[,4:3])
binary[[i]] = hs.t>sort(ex.h)[ceiling(0.1*length(ex.h))];
plot(binary[[i]])
points(test[,4:3], col ='purple', pch = 20)

bg <- rad_bg(test[,4:3], climondbioclim[[1]], radius = 2000, n = 200)
bg.e <- raster::extract(hs.t, bg[,4:3]);
ev <- evaluate(ex.h, bg.e)
print(ev)
ev.auc[[i]] = ev@auc;

bg.bin <- raster::extract(binary[[i]], bg[,4:3]);
ex.bin <- raster::extract(binary[[i]], test[,4:3]);
ev.bin <- evaluate(ex.bin, bg.bin)
print(ev.bin)
bin.auc[[i]] = ev.bin@auc
}
bin.stack = stack(unlist(binary))
bin.sum = sum(bin.stack)
bin.weightave = sum(bin.stack * unlist(bin.auc))/sum(unlist(bin.auc))
plot(bin.sum>50); #bootstrap consensus
plot(bin.weightave>0.5); #weighted (bin.auc) consensus
points(data.ex[,4:3], pch = 20, cex =0.5)



## End(Not run)

rsh249/vegdistmod documentation built on May 28, 2019, 3:31 a.m.