Dear list,

I'm using the dismo package from R-forge to model the distribution of a invasive sp of moth.
I have to use the stack function with the 14 asci files that are my predictor variables, and this is the code.
predictors <- stack(list.files("America", "*.asc$", full.names=TRUE))
then when I ask for predictors it says:
class       : RasterStack 
filename    :  
nlayers     : 14 
nrow        : 3426 
ncol        : 3671 
ncell       : 12576846 
projection  : NA 
min value   : NA NA NA NA NA NA NA NA NA NA NA NA NA NA 
max value   : NA NA NA NA NA NA NA NA NA NA NA NA NA NA 
xmin        : -179.1807 
xmax        : -26.22232 
ymin        : -59.5 
ymax        : 83.25 
xres        : 0.04166667 
yres        : 0.04166667 

But then when I try to make the prediction with any of the methods available (maxent, randomForest or boost regression trees) it gives an error 

Error in .local(object, ...) : missing layers (or wrong names) 

even with the example code.. it says the same. I have checked all the names, in the folder and in the file and all the names are correctly spelled, and they are all in the same folder, same order.


This is the code I'm using

Agcingu <- read.csv("swd/Ag_cinswd.csv")
background <- read.csv("swd/background_swd.csv")
vars <- names(Agcingu)[4:17]
head(vars)
vars

# combine presence and background data
train <- rbind(Agcingu, background)[,vars]
head(train)

# vector indicating presence or absence
p <- c(rep(1, nrow(Agcingu)), rep(0, nrow(background)))

# fit maxent model
me <- maxent(x=train, p=p)

# make a stack of predictor rasters
predictors <- stack(list.files("America", "*.asc$", full.names=TRUE))
predictors

# predict the maxent model to the predictor stack
mep <- predict(me, predictors)
Error in .local(object, ...) : missing layers (or wrong names)

#brt data
brttrain <- rbind(Agcingu, backg)[,vars ]
p <- c(rep(1, nrow(Agcingu)), rep(0, nrow(backg)))
#makes the sum of the weight of the presence points equal the sum f the weights of the background points
weights <- c(rep(1, nrow(Agcingu)), rep(nrow(Agcingu)/nrow(backg), nrow(backg)))

# make brt model
brt <- gbm.step(cbind(p,brttrain), 1:length(train)+1, 1, site.weights=weights)
brtp <- predict(predictors, brt, n.trees=brt$n.trees, progress='text', type="response") 
here it stops at 2% and gives
In addition: Warning message:
closing unused connection 3 (C:/DOCUME~1/balleste/LOCALS~1/Temp/R_raster_tmp/raster_tmp_83109427816.gri)


Can anybody help me? Any idea to solve this problem will be very welcome and appreciated.

Thanks a lot in advanced,

Cheers,

Liliana Ballesteros.






      
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