banter (version 0.9.6)

addBanterDetector: Add a BANTER Detector Model

Description

Add a detector model to a BANTER classifier.

Usage

addBanterDetector(
  x,
  data,
  name,
  ntree,
  sampsize = 1,
  importance = FALSE,
  num.cores = 1
)
removeBanterDetector(x, name)

Value

a banter_model object with the detector model added or removed.

Arguments

x

a banter_model object.

data

detector data.frame or named list of detector data.frames. If a data.frame, then name must be provided.

name

detector name.

ntree

number of trees.

sampsize

number or fraction of samples to use in each tree. If < 1, then it will be used to select this fraction of the smallest sample size.

importance

retain importance scores in model? Defaults to FALSE and will be ignored if num.cores > 1.

num.cores

number of cores to use for Random Forest model. Set to NULL to use the maximum number detected on your system - 1.

Author

Eric Archer

References

Rankin, S., Archer, F., Keating, J. L., Oswald, J. N., Oswald, M. , Curtis, A. and Barlow, J. (2017), Acoustic classification of dolphins in the California Current using whistles, echolocation clicks, and burst pulses. Marine Mammal Science 33:520-540. doi:10.1111/mms.12381

Examples

data(train.data)
# initialize BANTER model with event data
bant.mdl <- initBanterModel(train.data$events)
# add the 'bp' (burst pulse) detector model
bant.mdl <- addBanterDetector(
  x = bant.mdl, 
  data = train.data$detectors$bp, 
  name = "bp",
  ntree = 50, sampsize = 1, num.cores = 1
)
bant.mdl

# remove the 'bp' detector model
bant.mdl <- removeBanterDetector(bant.mdl, "bp")
bant.mdl