Reputation: 1
I'm with spatstat package in R.I'm working on species distribution models mainly on point process models.
My goal is to evaluate the predictive performance of point process models of Gibbs, Log-Gaussian Cox, and many more, point processes.
I've fitted Gibbs models to my data and when doing prediction
predict(f.ppm, window = W, covariates=present$bio4)
an error occurs
Error in covariates[covnames.needed] : objet de type 'S4' non indicable
which means that object of type 'S4' cannot be identified.
present
yields:
class : RasterStack
dimensions : 694, 350, 242900, 5 (nrow, ncol, ncell, nlayers)
resolution : 1000, 1000 (x, y)
extent : 248674.5, 598674.5, 683042.6, 1377043 (xmin, xmax, ymin, ymax)
crs : +proj=utm +zone=31 +ellps=WGS84 +units=m +no_defs
names : bio4, llds, mimq, pet, SV
min values : 10.02888, 4.00000, 95.45561, 1293.52945, 0.00000
max values : 25.00000, 8.00000, 222.91389, 2062.00000, 7.80565
Upvotes: 0
Views: 147
Reputation: 2973
Presumably your object f.ppm
is a fitted model of class ppm
. Then your code is invoking the predict
method for this class, predict.ppm
. The help file for predict.ppm
states that the argument covariates
should be "either a data frame, or a list of pixel images of class im
". Your data present$bio4
does not conform to either of these formats. You will need to convert the data to the required format.
Upvotes: 1