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Fit and validate Maximum Entropy models

Source: R/fit_max.R
fit_max.Rd

Fit and validate Maximum Entropy models

Usage

fit_max(
  data,
  response,
  predictors,
  predictors_f = NULL,
  fit_formula = NULL,
  partition,
  background = NULL,
  thr = NULL,
  clamp = TRUE,
  classes = "default",
  pred_type = "cloglog",
  regmult = 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")

fit_formula

formula. A formula object with response and predictor variables. See maxnet.formula function from maxnet package. Note that the variables used here must be consistent with those used in response, predictors, and predictors_f arguments. Default NULL.

partition

character. Column name with training and validation partition groups.

background

data.frame. Database including only those rows with 0 values in the response column and the predictors variables. All column names must be consistent with data. Default NULL

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 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 values 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 thresholds if no threshold is specified.

clamp

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

classes

character. A single feature of any combinations of them. Features are symbolized by letters: l (linear), q (quadratic), h (hinge), p (product), and t (threshold). Usage classes = "lpq". Default "default" (see details).

pred_type

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

regmult

numeric. A constant to adjust regularization. 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: Performance metrics (see sdm_eval). Threshold dependent metrics are calculated based on the threshold specified in thr argument.

  • data_ens: Predicted suitability for each test partition based on the best model. This database is used in fit_ensemble

Details

When the argument “classes” is set as default MaxEnt will use different features combination depending of the number of presences (np) with the follow rule: if np < 10 classes = "l", if np between 10 and 15 classes = "lq", if np between 15 and 80 classes = "lqh", and if np >= 80 classes = "lqph"

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).

See also

fit_gam, fit_gau, fit_gbm, fit_glm, fit_net, fit_raf, and fit_svm.

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
# Note 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 = 5)
)
abies2

backg2 <- part_random(
  data = backg,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 5)
)
backg2

max_t1 <- fit_max(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  background = backg2,
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
  clamp = TRUE,
  classes = "default",
  pred_type = "cloglog",
  regmult = 1
)
length(max_t1)
max_t1$model
max_t1$predictors
max_t1$performance
max_t1$data_ens
} # }