Similar question was answered here; however, this problem is a bit different for which I cannot apply that solution. I have fitted maxent using site-with-data format. The problem is I cannot project the fitted model. The output of m1 in the path D:/maxent looks fine. I suspect this two error (below) is related with rJava, but I don't know the solution. Please see my codes below:
> m1 <- maxent(x = d, p = id, path = "D:/maxent",
args = c("-P", "noautofeature", "nolinear", "noquadratic", "nothreshold",
"noproduct", "betamultiplier=1", "replicates=10", "crossvalidate"))
# here d is a dataframe containing 11213 rows and 20 predictor columns (with numeric values), id is a vector containing numeric values of 1 and 0 (representing species presence and absence)
Loading required namespace: rJava
> plot(m1, xlim=c(0,100))
Error in as.double(y) :
cannot coerce type 'S4' to vector of type 'double'
> ras <- raster("E:/bio12.tif") # raster to project the fitted model 'm1'
> pred.m1 <- raster::predict(m1, ras)
Error in .local(object, ...) : missing layers (or wrong names)
Here is the properties of raster file
> ras
class : RasterLayer
dimensions : 4292, 4936, 21185312 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : 112.8917, 154.025, -43.75833, -7.991667 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : E:/Predictors_grasshoppers/selected.predictors/bio12.tif
names : bio12
values : 79, 7625 (min, max)
Update: I have tried using single quote in m1 and the problem is still there.
> m2 <- maxent(x = d, p = id, path = 'D:/PhD related/Historic climate data Australia/maxent2',
args = c('-P', 'noautofeature', 'nolinear', 'noquadratic', 'nothreshold',
'noproduct', 'betamultiplier=1', 'replicates=10', 'crossvalidate'))
Following the comment of @Bappa Das I found the solution. To project maxent fitted model into geographic space one should use a raster stack (not a single raster) containing variables that were used during the model fitting process. The order and name of predictors in the raster stack should be same as they were in fitted model.