ENMevaluate_b | R Documentation |
This function is a wrapper for ENMeval::ENMevaluate. See ?ENMeval::ENMevaluate for details It works with a named list of species occurrence data (occ.l) and a list of cropped environmental variables (a.calib.l) for model tuning.
ENMevaluate_b(
occ.l,
a.calib.l,
bg.coords.l = NULL,
occ.grp.l = NULL,
bg.grp.l = NULL,
RMvalues = seq(0.5, 4.5, 0.5),
fc = c("L", "P", "Q", "H", "LP", "LQ", "LH", "PQ", "PH", "QH", "LPQ", "LPH", "LQH",
"PQH", "LPQH"),
categoricals = NULL,
n.bg = 10000,
method = "block",
algorithm = "maxnet",
overlap = FALSE,
aggregation.factor = c(2, 2),
kfolds = NA,
bin.output = FALSE,
clamp = TRUE,
rasterPreds = TRUE,
parallel = FALSE,
numCores = NULL,
progbar = TRUE,
updateProgress = FALSE,
resultsOnly = F,
...
)
occ.l |
list of species occurrence data |
a.calib.l |
list of predictors (cropped environmental variables) for model tuning. Used in model calibration. Argument 'x' of dismo::maxent. Raster* object or SpatialGridDataFrame, containing grids with predictor variables. These will be used to extract values from for the point locations. Can also be a data.frame, in which case each column should be a predictor variable and each row a presence or background record. |
bg.coords.l |
list of background localities. Two-column matrix or data.frame of longitude and latitude (in that order) of background localities (required for 'user' method). |
occ.grp.l |
list containing a vector of bins of occurrence localities (required for 'user' method) for each species. |
bg.grp.l |
list containing a vector of bins of occurrence localities (required for 'user' method) for each species. |
RMvalues |
Vector of (non-negative) values to use for the regularization multiplier. |
fc |
Character vector of feature class combinations to be included in analysis. |
categoricals |
Vector indicating which (if any) of the input environmental layers are categorical. |
n.bg |
The number of random background localities to draw from the study extent. |
method |
Character string designating the method used for data partitioning. Choices are: |
algorithm |
Character vector. Use |
overlap |
logical; If |
aggregation.factor |
List giving the factor by which the original input grid should be aggregated for checkerboard partitioning methods (see details and |
kfolds |
Number of bins to use in the k-fold random method of data partitioning. |
bin.output |
logical; If |
clamp |
logical; If |
rasterPreds |
logical; If |
parallel |
logical; If |
numCores |
numeric; indicates the number of cores to use if running in parallel. If |
progbar |
logical; used internally. |
updateProgress |
logical; used internally. |
resultsOnly |
logical; If TRUE, only results, 'occ.pts', 'bg.pts', 'occ.grp', and 'bg.grp' are returned. The 'predictions' and 'models' slots will be empty. Can be used to optimize allocated RAM memory when 'ENMevaluate' objects are too large. However it will not be possible to check MaxEnt models and predictions. |
... |
character vector; use this to pass other arguments (e.g., prevalence) to the 'maxent' call. Note that not all options are functional or relevant. |
ENMevaluate
## Not run:
ENMeval.res.lst <- ENMevaluate_b(occ.locs, occ.b.env, parallel = T , numCores = 7)
## End(Not run)
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