CODEWITHSUNDEEP
rmaxent
S. Smythe
S. Smythe

Reputation: 23

Maxent: Error in match.names(clabs, names(xi)) : names do not match previous names

I'm attempting to predict elk elevational selection using elk location points (Elk) relative to an NED elevational raster (Elev) using maxent in R. Both are projected in NAD83 using lat/long, and I've cropped the raster to get around memory issues in R.

I've divided the Elk dataset into 5 groups, 4 of which are now the training group (ElkTrain) and 1 I've reserved as the test group (ElkTest). I also created my own background data (NonElk) with its own training and test data (NonElkTrain, NonElkTest). I'm running into this error (with and without using my background data) and I can't find anyone discussing this relative to maxent or when using only one dataframe:

> Max <- maxent(x=Elev, p=ElkTrain, a=NonElkTrain)

or

> Max <- maxent(x=Elev, p=ElkTrain, a=NULL, nbg=5000, factors=NULL, removeDuplicates=TRUE)

Error in match.names(clabs, names(xi)) : names do not match previous names

In addition: Warning message:

In .local(x, p, ...) : 1 (0.02%) of the presence points have NA predictor values

Since I'm only using one dataframe (ElkTrain), what names aren't matching?

Upvotes: 2

Views: 493

Answers (1)

Robert Hijmans
Robert Hijmans

Reputation: 47401

That is a bug that occurs when you make a maxent model with only one predictor. You can see it with the data from the example in ?maxent

library(dismo)
# example data
predictors <- stack(list.files(path=paste(system.file(package="dismo"), '/ex', sep=''), pattern='grd', full.names=TRUE ))
occ <- read.table(paste(system.file(package="dismo"), '/ex/bradypus.csv', sep=''), header=TRUE, sep=',')[,-1]
bg <- randomPoints(predictors, 1000)

# this works
me <- maxent(x=predictors[[1:2]], p =occ)

# fails
me <- maxent(x=predictors[[1]], p =occ)
#Error in match.names(clabs, names(xi)) : 
#  names do not match previous names

This is because with a single layer, the matrix is dropped (the cause of many R bugs...), illustrated here:

extract(predictors[[1:2]], occtrain[1:2,])
#     bio1 bio12
#[1,]  263  1639
#[2,]  263  1639

extract(predictors[[1]], occtrain[1:2,])
#[1] 263 263

I will fix that. But here is are two work-arounds.

= Either make a single layer RasterStack (as suggested by you); the simplest approach:

prd <- stack(predictors[[1]])
me <- maxent(x=prd, p =occ)

= Or make a data.frame with extracted raster values for presence and background points:

abs <- cbind(pa=0, bio1=extract(predictors[[1]], bg))
prs <- cbind(pa=1, bio1=extract(predictors[[1]], occ))

and use these data to build the maxent model

x <- data.frame(rbind(prs, abs))
m <- maxent(x[,2,drop=F], p=x[,1,drop=F] )

p <- predict(predictors, m)
plot(p)

Upvotes: 1

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