Fit and validate Neural Networks models
Usage
fit_net(
data,
response,
predictors,
predictors_f = NULL,
fit_formula = NULL,
partition,
thr = NULL,
size = 2,
decay = 0.1
)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 (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. Defaul NULL.
- partition
character. Column name with training and validation partition groups.
- 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 specified. 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 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.
- size
numeric. Number of units in the hidden layer. Can be zero if there are skip-layer units. Default 2.
- decay
numeric. Parameter for weight decay. Default 0.1.
Value
A list object with:
model: A "nnet.formula" "nnet" class object from nnet 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 metric 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
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 = 10)
)
abies2
nnet_t1 <- fit_net(
data = abies2,
response = "pr_ab",
predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
predictors_f = c("landform"),
partition = ".part",
thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
fit_formula = NULL
)
nnet_t1$model
nnet_t1$predictors
nnet_t1$performance
nnet_t1$data_ens
# Using bootstrap partition method and only with presence-absence
# and get performance for several method
abies2 <- part_random(
data = abies,
pr_ab = "pr_ab",
method = c(method = "boot", replicates = 10, proportion = 0.7)
)
abies2
nnet_t2 <- fit_net(
data = abies2,
response = "pr_ab",
predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
predictors_f = c("landform"),
partition = ".part",
thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
fit_formula = NULL
)
nnet_t2
} # }