Fit and validate Gaussian Process models

Usage

fit_gau(
  data,
  response,
  predictors,
  predictors_f = NULL,
  background = NULL,
  partition,
  thr = 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 type). Usage predictors_f = c("landform")

background

data.frame. Database with response column only with 0 and predictors variables. All column names must be consistent with data

partition

character. Column name with training and validation partition groups.

thr

character. Threshold used to get binary suitability values (i.e. 0,1), useful for threshold-dependent performance metrics. It is possible to use more than one threshold type. A vector must be provided 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 the 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 criteria if none is specified.

Value

A list object with:

model: A "graf" class object. 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 metrics (see sdm_eval). Threshold dependent metrics are calculated based on the threshold criteria specified in the argument.
data_ens: Predicted suitability for each test partition. This database is used in fit_ensemble

Examples

if (FALSE) { # \dontrun{
data("abies")

# Using k-fold partition method
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 3)
)
abies2

bg <- abies2
bg$pr_ab <- 0


gaup_t1 <- fit_gau(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  background = bg,
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen")
)

gaup_t1$model
gaup_t1$predictors
gaup_t1$performance
gaup_t1$data_ens

# Using bootstrap partition method only with presence-absence
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "boot", replicates = 5, proportion = 0.7)
)
abies2

gaup_t2 <- fit_gau(
  data = abies2,
  response = "pr_ab",
  predictors = c("ppt_jja", "pH", "awc"),
  predictors_f = c("landform"),
  partition = ".part",
  thr = c(type = c("lpt", "max_sens_spec", "sensitivity"), sens = "0.8")
)
gaup_t2
} # }

Fit and validate Gaussian Process models

Usage

Arguments

Value

See also

Examples