ellipsoid_omr

Compute the omission rate of ellipspoid models

ellipsoid_omr(
  env_data,
  env_test = NULL,
  env_bg,
  cf_level,
  mve = TRUE,
  proc = FALSE,
  proc_iter = 100,
  rseed = TRUE
)

Arguments

env_data	A data frame with the environmental data.
env_test	A data frame with the environmental testing data. The default is NULL if given the selection process will show the p-value of a binomial test.
env_bg	Environmental data to compute the approximated prevalence of the model. The data should be a sample of the environmental layers of the calibration area.
cf_level	Proportion of points to be included in the ellipsoids. This parameter is equivalent to the error (E) proposed by Peterson et al. (2008).
mve	A logical value. If TRUE a minimum volume ellipsoid will be computed using the function cov.rob of the MASS package. If False the covariance matrix of the input data will be used.
proc	Logical if TRUE a partial roc test will be run.
proc_iter	Numeric. The total number of iterations for the partial ROC bootstrap.
rseed	Logical. Whether or not to set a random seed for partial roc bootstrap. Default TRUE.

Value

A data.frame with 5 columns: i) "fitted_vars" the names of variables that were fitted; ii) "om_rate" omission rates of the model; iii) "bg_prevalence" approximated prevalence of the model see details section.

Examples

if (FALSE) {
# Bioclimatic layers path
wcpath <- list.files(system.file("extdata/bios",
                                package = "ntbox"),
                    pattern = ".tif$",full.names = TRUE)
# Bioclimatic layers
wc <- raster::stack(wcpath)
# Occurrence data for the giant hummingbird (Patagona gigas)
pg <- utils::read.csv(system.file("extdata/p_gigas.csv",
                                  package = "ntbox"))
# Split occs in train and test
pgL <- base::split(pg,pg$type)
pg_train <- pgL$train
pg_test <- pgL$test
# Environmental data for training and testing
pg_etrain <- raster::extract(wc,pg_train[,c("longitude",
                                            "latitude")],
                             df=TRUE)
pg_etrain <- pg_etrain[,-1]
pg_etest <- raster::extract(wc,pg_test[,c("longitude",
                                          "latitude")],
                            df=TRUE)
pg_etest <- pg_etest[,-1]

# Non-correlated variables
env_varsL <- ntbox::correlation_finder(cor(pg_etrain),
                                       threshold = 0.8,
                                       verbose = F)
env_vars <- env_varsL$descriptors
env_bg <- raster::sampleRandom(wc,10000)
ellip_eval <- ellipsoid_omr(env_data=pg_etrain[,c("bio01","bio07","bio12")],
                            env_test=pg_etest[,c("bio01","bio07","bio12")],
                            env_bg = env_bg[,c("bio01","bio07","bio12")],
                            cf_level = 0.97,
                            mve=TRUE,proc=TRUE,
                            proc_iter=100,rseed=TRUE)
print(ellip_eval)
}