ellipsoid_selection: Performs models selection for ellipsoid models
Source:R/ellipsoid_selection.R
ellipsoid_selection.RdThe function performs model selection for ellipsoid models using three criteria: a) the omission rate, b) the significance of partial ROC and binomial tests and c) the AUC value.
Usage
ellipsoid_selection(
env_train,
env_test = NULL,
env_vars,
nvarstest,
level = 0.95,
mve = TRUE,
env_bg = NULL,
omr_criteria,
parallel = FALSE,
ncores = NULL,
comp_each = 100,
proc = FALSE,
proc_iter = 100,
rseed = TRUE
)Arguments
- env_train
A data frame with the environmental training data.
- env_test
A data frame with the environmental testing data. Default is NULL.
- env_vars
A vector with the names of environmental variables used in the selection process. To help choosing which variables to use see
correlation_finder.- nvarstest
A vector indicating the number of variables to fit the ellipsoids during model selection.
- level
Proportion of points to be included in the ellipsoids, equivalent to the error (E) proposed by Peterson et al. (2008).
- mve
Logical. If
TRUE, a minimum volume ellipsoid will be computed. usingcov.robfrom MASS. IfFALSE, the covariance matrix of the input data will be used.- env_bg
Environmental data to compute the approximated prevalence of the model, should be a sample of the environmental layers of the calibration area.
- omr_criteria
Omission rate criteria: the allowable omission rate for the selection process. Default is NULL (see details).
- parallel
Logical. If
TRUE, computations will run in parallel. Default isF.- ncores
Number of cores to use for parallel processing. Default uses all available cores minus one.
- comp_each
Number of models to run in each job in parallel computation. Default is 100.
- proc
Logical. If
TRUE, a partial ROC test will be run.- proc_iter
Numeric. Total iterations for the partial ROC bootstrap.
- rseed
Logical. If
TRUE, set a random seed for partial ROC bootstrap. Default isTRUE.
Value
A data.frame with the following columns:
"fitted_vars": Names of variables that were fitted.
"nvars": Number of fitted variables
"om_rate_train": Omission rate of the training data.
"non_pred_train_ids": Row IDs of non-predicted training data.
"om_rate_test"': Omission rate of the testing data.
"non_pred_test_ids": Row IDs of non-predicted testing data.
"bg_prevalence": Approximated prevalence of the model (see details).
"pval_bin": p-value of the binomial test.
"pval_proc": p-value of the partial ROC test.
"env_bg_paucratio": Environmental partial AUC ratio value.
"env_bg_auc": Environmental AUC value.
"mean_omr_train_test": Mean value of omission rates (train and test).
"rank_by_omr_train_test": Rank value of importance in model selection by omission rate.
"rank_omr_aucratio": Rank value by AUC ratio.
Details
Model selection occurs in environmental space (E-space). For each variable
combination specified in nvarstest, the omission rate (omr) in E-space is
computed using inEllipsoid function.
Results are ordered by omr of the testing data. If env_bg is provided,
an estimated prevalence is computed and results are additionally ordered
by partial AUC. Model selection can be run in parallel.
For more details and examples go to ellipsoid_omr help.
References
Peterson, A.T. et al. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell. 213, 63–72. doi:10.1016/j.ecolmodel.2007.11.008
Author
Luis Osorio-Olvera luismurao@gmail.com
Examples
# \donttest{
library(tenm)
data("abronia")
tempora_layers_dir <- system.file("extdata/bio",package = "tenm")
abt <- tenm::sp_temporal_data(occs = abronia,
longitude = "decimalLongitude",
latitude = "decimalLatitude",
sp_date_var = "year",
occ_date_format="y",
layers_date_format= "y",
layers_by_date_dir = tempora_layers_dir,
layers_ext="*.tif$")
abtc <- tenm::clean_dup_by_date(abt,threshold = 10/60)
future::plan("multisession",workers=2)
abex <- tenm::ex_by_date(this_species = abtc,train_prop=0.7)
abbg <- tenm::bg_by_date(this_species = abex,
buffer_ngbs=10,n_bg=50000)
future::plan("sequential")
varcorrs <- tenm::correlation_finder(environmental_data =
abex$env_data[,-ncol(abex$env_data)],
method = "spearman",
threshold = 0.8,
verbose = FALSE)
#> Warning: the standard deviation is zero
edata <- abex$env_data
etrain <- edata[edata$trian_test=="Train",] |> data.frame()
etest <- edata[edata$trian_test=="Test",] |> data.frame()
bg <- abbg$env_bg
res1 <- tenm::ellipsoid_selection(env_train = etrain,
env_test = etest,
env_vars = varcorrs$descriptors,
nvarstest = 3,
level = 0.975,
mve = TRUE,
env_bg = bg,
omr_criteria = 0.1,
parallel = FALSE,proc = TRUE)
#> -------------------------------------------------------------------
#> **** Starting model selection process ****
#> -------------------------------------------------------------------
#>
#> A total number of 84 models will be created for combinations of 9 variables taken by 3
#>
#> -------------------------------------------------------------------
#> **A total number of 84 models will be tested **
#>
#> -------------------------------------------------------------------
#> 54 models passed omr_criteria for train data
#> 9 models passed omr_criteria for test data
#> 9 models passed omr_criteria for train and test data
head(res1)
#> fitted_vars nvars om_rate_train non_pred_train_ids om_rate_test
#> 1 bio_01,bio_04,bio_07 3 0.06250 18,31 0
#> 2 bio_01,bio_03,bio_04 3 0.06250 3,18 0
#> 3 bio_01,bio_02,bio_04 3 0.09375 3,18,31 0
#> 4 bio_01,bio_03,bio_12 3 0.06250 3,18 0
#> 5 bio_01,bio_07,bio_12 3 0.06250 18,31 0
#> 6 bio_04,bio_07,bio_12 3 0.06250 21,28 0
#> non_pred_test_ids bg_prevalence pval_bin pval_proc env_bg_paucratio
#> 1 0.4661502 0 0 1.490142
#> 2 0.4692861 0 0 1.447998
#> 3 0.4809998 0 0 1.445395
#> 4 0.4851503 0 0 1.387971
#> 5 0.4973252 0 0 1.381564
#> 6 0.7312304 0 0 1.326297
#> env_bg_auc mean_omr_train_test rank_by_omr_train_test rank_omr_aucratio
#> 1 0.8036213 0.031250 1 1
#> 2 0.7617325 0.031250 2 2
#> 3 0.7636137 0.046875 6 3
#> 4 0.6886475 0.031250 3 4
#> 5 0.7197813 0.031250 4 5
#> 6 0.6970550 0.031250 5 6
# }