Fit and validate Maximum Entropy models with exploration of hyper-parameters that optimize performance

Usage

tune_max(
  data,
  response,
  predictors,
  predictors_f = NULL,
  background = NULL,
  partition,
  grid = NULL,
  thr = NULL,
  metric = "TSS",
  clamp = TRUE,
  pred_type = "cloglog",
  n_cores = 1
)

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 variable column only containing 0 values, and predictors variables. All column names must be consistent with data

partition

character. Column name with training and validation partition groups.

grid

data.frame. A data frame object with algorithm hyper-parameters values to be tested. It is recommended to generate this data.frame with the grid() function. Hyper-parameters needed for tuning are 'regmult' and 'classes' (any combination of following letters l -linear-, q -quadratic-, h -hinge-, p -product-, and t -threshold-).

thr

character. Threshold used to get binary suitability values (i.e. 0,1)., needed for threshold-dependent performance metrics. More than one threshold type can be used. It is necessary to provide a vector for this argument. The following threshold types are available:

lpt: The highest threshold at which there is no omission.
equal_sens_spec: Threshold at which 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, a default of 0.9 will be used.

If more than one threshold type is used, concatenate them, 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 thresholds if no threshold is specified.

metric

character. Performance metric used for selecting the best combination of hyper -parameter values. One of the following metrics can be used: SORENSEN, JACCARD, FPB, TSS, KAPPA, AUC, and BOYCE. TSS is used as default.

clamp

logical. If TRUE, predictors and features are restricted to the range seen during model training.

pred_type

character. Type of response required available "link", "exponential", "cloglog" and "logistic". Default "cloglog"

n_cores

numeric. Number of cores use for parallelization. Default 1

Value

A list object with:

model: A "maxnet" class object from maxnet package. 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: Hyper-parameters values and performance metrics (see sdm_eval) for the best hyper-parameters combination.
hyper_performance: Performance metrics (see sdm_eval) for each combination of the hyper-parameters.
data_ens: Predicted suitability for each test partition based on the best model. This database is used in fit_ensemble

Details

When presence-absence (or presence-pseudo-absence) data are used in data argument in addition to background points, the function will fit models with presences and background points and validate with presences and absences. This procedure makes maxent comparable to other presences-absences models (e.g., random forest, support vector machine). If only presences and background points data are used, function will fit and validate model with presences and background data. If only presence-absences are used in data argument and without background, function will fit model with the specified data (not recommended).

Examples

if (FALSE) { # \dontrun{
data("abies")
data("backg")
abies # environmental conditions of presence-absence data
backg # environmental conditions of background points

# Using k-fold partition method
# Remember that the partition method, number of folds or replications must
# be the same for presence-absence and background points datasets
abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 3)
)
abies2

set.seed(1)
backg <- dplyr::sample_n(backg, size = 2000, replace = FALSE)
backg2 <- part_random(
  data = backg,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 3)
)
backg


gridtest <-
  expand.grid(
    regmult = seq(0.1, 3, 0.5),
    classes = c("l", "lq", "lqh")
  )

max_t1 <- tune_max(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  background = backg2,
  grid = gridtest,
  thr = "max_sens_spec",
  metric = "TSS",
  clamp = TRUE,
  pred_type = "cloglog",
  n_cores = 2 # activate two cores to speed up this process
)

length(max_t1)
max_t1$model
max_t1$predictors
max_t1$performance
max_t1$data_ens
} # }