Thanks. Any thoughts on doing the inverse? ]]>

Thanks for this article, still helpful five years later! :)

]]>Thanks ]]>

Thanks for sharing this short tips pvand.

Cheers! ]]>

#——————————————————————–#

# NT1 – UNIVARIATE EXTRAPOLATION

#——————————————————————–#

# UD(ij) = min(

MaxMin <- matrix(NA, nrow=2, ncol=ncol(refdat))

colnames(MaxMin) <- colnames(refdat); rownames(MaxMin) = c("max", "min")

for(i in 1:ncol(refdat)){

MaxMin[1,i] <- max(refdat[,i], na.rm=TRUE) # max value of each variable in the reference matrix

MaxMin[2,i] <- min(refdat[,i], na.rm=TRUE) # min value of each variable in the reference matrix

}

UDs <- matrix(NA, nrow(prodat), ncol(prodat))

for(j in 1:ncol(prodat)){

for(i in 1:nrow(prodat)){

UDs[i,j] <- (min(c(

(prodat[i,j] – MaxMin[2,j]),(MaxMin[1,j] – prodat[i,j]), 0), na.rm=TRUE))/

(MaxMin[1,j] – MaxMin[2,j])

}

}

UDs <- data.frame(UDs)

NT1 <- rowSums(UDs, na.rm=TRUE)

# Create and plot the raster layer NT1

mah.proR <- read.asciigrid(fname=pro[1]) # coordinates from a single grid

NT1 <- data.frame(NT1)

mah.proR@data <- NT1 # assing data values from above

library(raster)

NT1rast <- raster(mah.proR)

plot(NT1rast, col=rainbow(100), ylim=c(-35,-20), xlim=c(15,35))

Nice article by the way. I have to admit that I know very little about cryptogamic covers, but it provides in interesting read. I was particularly interested to learn about the relative importance of cryptogamic cover in the two tropical forests basins in terms of flux intensity of carbon net uptake (if I understand it correctly).

]]>You can create probability distribution maps using the predict function from dismo, see http://www.inside-r.org/packages/cran/dismo/docs/predict. Maxent is one of the supported models, but in case you have models not directly supported by the predict function from dismo, you can often use the predict function from raster: http://www.inside-r.org/packages/cran/raster/docs/predict. ]]>

I am new in R and Species distribution modelling but have good experience in GIS and Remote Sensing. I am using MESS to compute dengue risk map using dismo package in R but dont know how result can be exported from R and also how to interpret and export MESS index value to excel and or ARC GIS environment. ]]>

I was wondering if you could help me. When I run the import.wc.bil script it gives me the error that there are no zip files in the folder that contains my downloads. Do you know what could be causing this? I’m running windows and GRASS 7.0.

Thank you. ]]>

just for your information, there is now a new addon r.sample.category which wraps the r.random and r.mask workflow.

http://grass.osgeo.org/grass70/manuals/addons/r.sample.category.html