Fit and validate Artificial Neural Network models

Usage

fit_abund_net(
  data,
  response,
  predictors,
  predictors_f = NULL,
  fit_formula = NULL,
  partition,
  predict_part = FALSE,
  size,
  decay = 0,
  verbose = TRUE
)

Arguments

data: tibble or data.frame. Database with response, predictors, and partition values
response: character. Column name with species abundance.
predictors: character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). Usage predictors = c("temp", "precipt", "sand")
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 (e.g. formula(abund ~ temp + precipt + sand + landform)). 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.
predict_part: logical. Save predicted abundance for testing data. Default is FALSE.
size: numerical. The size of the hidden layer.
decay: numerial. Value for weight decay. Default 0.
verbose: logical. If FALSE, disables all console messages. Default TRUE

Value

A list object with:

model: A "ksvm" class object from kernlab 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: Averaged performance metrics (see adm_eval).
performance_part: Performance metrics for each replica and partition.
predicted_part: Observed and predicted abundance for each test partition.

Examples

if (FALSE) {
require(dplyr)

# Database with species abundance and x and y coordinates
data("sppabund")

# Extract data for a single species
some_sp <- sppabund %>%
  dplyr::filter(species == "Species one") %>%
  dplyr::select(-.part2, -.part3)

# Explore reponse variables
some_sp$ind_ha %>% range()
some_sp$ind_ha %>% hist()

# Here we balance number of absences
some_sp <-
  balance_dataset(some_sp, response = "ind_ha", absence_ratio = 0.2)

# Fit a NET model
mnet <- fit_abund_net(
  data = some_sp,
  response = "ind_ha",
  predictors = c("bio12", "elevation", "sand"),
  predictors_f = c("eco"),
  partition = ".part",
  size = 32,
  decay = 0.1,
  predict_part = TRUE
)

mnet
}