Fit and validate Domain models

Usage

fit_dom(
  data,
  response,
  predictors,
  predictors_f = NULL,
  partition,
  thr = NULL,
  fit_formula = NULL
)

Arguments

data

data.frame. Database with response (0,1) and predictors values.

response

character. Column name with species absence-presence data (0,1).

predictors

character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). Usage predictors = c("aet", "cwd", "tmin")

predictors_f

character. Vector with the column names of qualitative predictor variables (i.e. ordinal or nominal variables; factors). Usage predictors_f = c("landform")

partition

character. Column name with training and validation partition groups.

thr

character. Threshold used to get binary suitability values (i.e. 0,1). This is useful for threshold-dependent performance metrics. It is possible to use more than one threshold type. It is necessary to provide a vector for this argument. The following threshold criteria are available:

lpt: The highest threshold at which there is no omission.
equal_sens_spec: Threshold at which the sensitivity and specificity are equal.
max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS).
max_jaccard: The threshold at which the Jaccard index is the highest.
max_sorensen: The threshold at which the Sorensen index is highest.
max_fpb: The threshold at which FPB (F-measure on presence-background data) is highest.
sensitivity: Threshold based on a specified sensitivity value. Usage thr = c('sensitivity', sens='0.6') or thr = c('sensitivity'). 'sens' refers to sensitivity value. If a sensitivity value is not specified, the default used is 0.9.

If more than one threshold type is used they must be concatenated, e.g., thr=c('lpt', 'max_sens_spec', 'max_jaccard'), or thr=c('lpt', 'max_sens_spec', 'sensitivity', sens='0.8'), or thr=c('lpt', 'max_sens_spec', 'sensitivity'). Function will use all threshold types if none is specified.

fit_formula

formula. A formula object with response and predictor variables (e.g. formula(pr_ab ~ aet + ppt_jja + pH + awc + depth + landform)). Note that the variables used here must be consistent with those used in response, predictors, and predictors_f arguments

Value

A list object with:

model: A tibble with presences. This object can be used for predicting.
predictors: A tibble with quantitative (c column names) and qualitative (f column names) variables use for modeling.
performance: Performance metric (see sdm_eval). Threshold dependent metrics are calculated based on the threshold specified in the argument.
data_ens: Predicted suitability for each test partition. This database is used in fit_ensemble

Details

This function fits and validates Domain models. The Domain model is a simple model that uses the Gower distance to calculate environmental similarity between the presence data and test data (Carpenter et al., 1993). Gower range of values area based on presences data. Gower distance are transformed to max(0, 1 - Gower). This involves subtracting the distance from 1 and then ensuring the result is not negative (clamping it at zero). Gower distance is calculated with map_env_dist function

This function fit and validate Domain models. The Domain model is a simple model that uses the Gower distance to calculate the similarity between the presences training and presence-absences test data.

References

Carpenter, G., Gillison, A.N., Winter, J., 1993. DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity & Conservation 2, 667–680

Examples

if (FALSE) { # \dontrun{
require(dplyr)
require(terra)

data("spp")
somevar <- system.file("external/somevar.tif", package = "flexsdm")
somevar <- terra::rast(somevar)

# Extract data
some_sp <- spp %>%
  filter(species == "sp2")

some_sp <-
  sdm_extract(
    data = some_sp,
    x = "x",
    y = "y",
    env_layer = somevar
  )

# Partition
some_sp <- part_random(
  data = some_sp,
  pr_ab = "pr_ab",
  method = c(method = "rep_kfold", folds = 3, replicates = 5)
)


##%######################################################%##
#                                                          #
####                Fit a Domain model                  ####
#                                                          #
##%######################################################%##
# Fit some models
mdom <- fit_dom(
  data = some_sp,
  response = "pr_ab",
  predictors = c("CFP_1", "CFP_2", "CFP_3", "CFP_4"),
  predictors_f = NULL,
  fit_formula = NULL,
  partition = ".part",
  thr = c("max_sens_spec")
)

mdom

# Predict model
ind_p <- sdm_predict(
  models = mdom,
  pred = somevar,
  thr = "max_sens_spec",
  con_thr = TRUE,
  predict_area = NULL
)
plot(ind_p$dom)

##%######################################################%##
#                                                          #
####             Explore Domain suitabiltiy             ####
####             in the environmental space             ####
#                                                          #
##%######################################################%##

p_extra(
  training_data = some_sp %>% dplyr::filter(pr_ab == 1), #select only presences
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  extra_suit_data = ind_p$dom$dom,
  projection_data = somevar,
  geo_space = FALSE,
  prop_points = 0.3,
  alpha_p = 0.8,
  color_p = "black",
  color_gradient = c("#000033", "#1400FF", "#C729D6", "#FF9C63", "#FFFF60")
)

p_extra(
  training_data = some_sp %>% dplyr::filter(pr_ab == 1), #select only presences
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  predictors = c("CFP_1", "CFP_2"), # Just the first two predictors
  extra_suit_data = ind_p$dom$dom > 0.96, # a binary map
  projection_data = somevar,
  geo_space = TRUE,
  prop_points = 0.4,
  alpha_p = 0.8,
  color_p = "black",
  color_gradient = c("#1400FF", "#C729D6")
)

} # }