model_maxent {wallace}

model_maxent Generate maxent.jar or maxnet model

Description

This functions generates maxent.jar or maxnet models using ENMeval 2.0 and user provided tuning parameters.

Usage

model_maxent(
  occs,
  bg,
  user.grp,
  bgMsk,
  rms,
  rmsStep,
  fcs,
  clampSel,
  algMaxent,
  catEnvs = NULL,
  parallel = FALSE,
  numCores = NULL,
  logger = NULL,
  spN = NULL
)

Arguments

occs

data frame of cleaned or processed occurrences obtained from components occs: Obtain occurrence data or, poccs: Process occurrence data.

bg

coordinates of background points to be used for modeling.

user.grp

a list of two vectors containing group assignments for occurrences (occs.grp) and background points (bg.grp).

bgMsk

a RasterStack or a RasterBrick of environmental layers cropped and masked to match the provided background extent.

rms

vector of range of regularization multipliers to be used in the ENMeval run.

rmsStep

step to be used when defining regularization multipliers to be used from the provided range.

fcs

feature classes to be tested in the ENMeval run.

clampSel

Boolean use of clamping in the model.

algMaxent

character. algorithm to be used in modeling. A selection of "maxnet" or "maxent.jar".

catEnvs

if categorical predictor variables are included must provide the names.

parallel

logical. Whether to use parallel in the generation of models. Default is FALSE

numCores

numeric. If using parallel how many cores to use. Default is NULL.

logger

Stores all notification messages to be displayed in the Log Window of Wallace GUI. Insert the logger reactive list here for running in shiny, otherwise leave the default NULL.

spN

character. Species name to be used for all logger messages.

Details

The function generates model in ENMeval using a user provided partition of occurrences from previous components in the GUI. User can activate clamping and input tuning arguments to be used for model building.

Value

Function returns an ENMevaluate object with all the evaluated models and a selection of appropriate fields.

Author(s)

Jamie M. Kass <[e-mail]>

Gonzalo E. Pinilla-Buitrago <[e-mail]>

See Also

ENMevaluate

Examples

## Not run: 
envs <- envs_userEnvs(rasPath = list.files(system.file("extdata/wc",
                                           package = "wallace"),
                      pattern = ".tif$", full.names = TRUE),
                      rasName = list.files(system.file("extdata/wc",
                                           package = "wallace"),
                      pattern = ".tif$", full.names = FALSE))
occs <- read.csv(system.file("extdata/Bassaricyon_alleni.csv",
                 package = "wallace"))
bg <- read.csv(system.file("extdata/Bassaricyon_alleni_bgPoints.csv",
               package = "wallace"))
partblock <- part_partitionOccs(occs, bg, method = 'block')
rms <- c(1:2)
rmsStep <- 1
fcs <- c('L', 'LQ')
m <- model_maxent(occs = occs, bg = bg, user.grp = partblock,
                  bgMsk = envs, rms = rms, rmsStep, fcs,
                  clampSel = TRUE, algMaxent = "maxnet",
                  parallel = FALSE)

## End(Not run)