swd_format | R Documentation |
swd_format It reshapes your occurrence and background information using the format samples with data (swd) to run maxent with an ordinary samples file.
swd_format(
env_layers,
nbg = NULL,
occs_points,
sp_name = "sp",
longitude,
latitude,
random_seed = NULL,
parallel = TRUE,
ncores = 4
)
env_layers |
A raster stack or brick with the environmental information. |
nbg |
Number of points for the background data. |
occs_points |
A data frame with longitude and latitude data. |
sp_name |
Species name. Default sp_name="sp". |
longitude |
Column name containing longitude data. |
latitude |
Column name containing latitude data. |
random_seed |
A numeric value for random seed |
parallel |
Run the process in parallel |
ncores |
Number of cores to run the parallel process |
The difference between the typical way of running MaxEnt models and it
is that the program doesn’t need to look in the environmental layers
to obtain values for the variables at the sample points or the background points.
each time that you call maxent (see maxent_call
).
One of the main advantages is that MaxEnt runs much faster see section SWD Format here
https://biodiversityinformatics.amnh.org/open_source/maxent/Maxent_tutorial2017.pdf
for details.
A list with the occurrence or/and background points in swd format. Note that coordinates correspond to pixel centroids
## Not run:
# Bioclimatic layers path
wcpath <- list.files(system.file("extdata/bios",
package = "ntbox"),
pattern = ".tif$",full.names = TRUE)
# Bioclimatic layers
wc <- raster::stack(wcpath)
# Occurrence data for the giant hummingbird (Patagona gigas)
pg <- utils::read.csv(system.file("extdata/p_gigas.csv",
package = "ntbox"))
pg_swd <- ntbox::swd_format(env_layers=wc,
nbg=10000,
occs_points =pg,
sp_name="p_gigas",
longitude = "longitude",
latitude = "latitude")
head(pg_swd$occs_swd)
head(pg_swd$bg_swd)
## End(Not run)
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