modelReport: Model Report

Description Usage Arguments Details Author(s) Examples

View source: R/modelReport.R

Description

Make a report that shows the main results.

Usage

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modelReport(
  model,
  folder,
  test = NULL,
  type = NULL,
  response_curves = FALSE,
  only_presence = FALSE,
  jk = FALSE,
  env = NULL,
  clamp = TRUE,
  permut = 10,
  factors = NULL
)

Arguments

model

SDMmodel object.

folder

character. The name of the folder in which to save the output. The folder is created in the working directory.

test

SWD object with the test locations, default is NULL.

type

character. The output type used for "Maxent" and "Maxnet" methods, possible values are "cloglog" and "logistic", default is NULL.

response_curves

logical, if TRUE it plots the response curves in the html output, default is FALSE.

only_presence

logical, if TRUE it uses only the range of the presence location for the marginal response, default is FALSE.

jk

logical, if TRUE it runs the jackknife test, default is FALSE.

env

stack. If provided it computes and adds a prediction map to the output, default is NULL.

clamp

logical for clumping during prediction, used for response curves and for the prediction map, default is TRUE.

permut

integer. Number of permutations, default is 10.

factors

list with levels for factor variables, see predict

Details

The function produces a report similar to the one created by MaxEnt software.

Author(s)

Sergio Vignali

Examples

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# If you run the following examples with the function example(), you may want
# to set the argument ask like following: example("modelReport", ask = FALSE)
# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd", full.names = TRUE)
predictors <- raster::stack(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords,
                   env = predictors, categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data, test = 0.2, only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet", data = train, fc = "lq")

# Create the report
## Not run: 
modelReport(model, type = "cloglog", folder = "my_folder", test = test,
            response_curves = TRUE, only_presence = TRUE, jk = TRUE,
            env = predictors, permut = 2)

## End(Not run)

SDMtune documentation built on July 17, 2021, 9:06 a.m.