Learn R Programming

⚠️There's a newer version (0.9.8) of this package.Take me there.

banter

Description

banter is a package for creating hierarchical acoustic event classifiers out of multiple call type detectors.

Installation

To install the latest version from GitHub:

# make sure you have Rtools installed
if(!require('devtools')) install.packages('devtools')
# install from GitHub
devtools::install_github('ericarcher/banter')

Tutorial

The BANTER model is initialized with a data.frame of events. There is one row per event and it must have a column called event.id which is a unique id for each event, and a column called species which assigns each event to a given species. Every other column in the data.frame will be used as a predictor variable for the events.
In the package, an example data.frame is in the train.data example data list as the $events element.

data(train.data)
bant.mdl <- initBanterModel(train.data$events)

Next, detector data is added to the initialized BANTER model object. Each detector is a data.frame with a column called event.id that associates the detected call with an event that the model was initialized with, and a call.id column that provides a unique identifier for each call. Every other column will be used as a predictor variable for the calls.
In the package, example data.frames for three detectors are provided in the $detectors element of the train.data example data list. Here is an example of adding the burst pulse (bp) detector.

bant.mdl <- addBanterDetector(
  bant.mdl, 
  data = train.data$detectors$bp, 
  name = "bp",
  ntree = 10, 
  sampsize = 1
)

The addBanterDetector function can be called repeatedly to add additional detectors. Alternatively, if the detectors are all in a named list, they can be added at once:

bant.mdl <- addBanterDetector(
  bant.mdl, 
  data = train.data$detectors, 
  ntree = 10, 
  sampsize = 1
)

Once all of the detectors have been added, then the full BANTER model is run:

bant.mdl <- runBanterModel(bant.mdl, ntree = 5000, sampsize = 3)

The model can be easily summarized:

summary(bant.mdl)

The actual randomForest model can be extracted for the event or detector models:

# extract event Random Forest model
event.rf <- getBanterModel(bant.mdl, "event")

# extract burst pulse (bp) Random Forest model
bp.rf <- getBanterModel(bant.mdl, "bp")

These can then be visualized using other tools, such as those in the rfPermute package:

library(rfPermute)
plotVotes(event.rf)

To predict novel data, it must be in a list with the event data in the $events element, and the detector data in a named list called $detectors:

data(test.data)
predict(bant.mdl, test.data)

Contact

Reference

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. Mar Mam Sci, 33: 520-540. doi:10.1111/mms.12381

version 0.9.2

  • Initial release

Copy Link

Version

Install

install.packages('banter')

Monthly Downloads

289

Version

0.9.3

License

GNU General Public License

Maintainer

Eric Archer

Last Published

July 10th, 2018

Functions in banter (0.9.3)

numCalls

Number and Proportion of Calls
initBanterModel

Initialize BANTER model
addBanterDetector

Add a BANTER Detector Model
banter_detector-class

Detector classification model
.rfFuncDetector

Detector randomForest function
banter_model-class

Events classification model
getDetectorNames

Detector Names
numEvents

Number of Events
banter-package

BioAcoustic EveNT ClassifiER
getSampSize

Sample Size
summary

BANTER Classifier Model Summary
predict

Predict BANTER events
test.data

Testing events and detectors
modelPctCorrect

Model Percent Correct
runBanterModel

Run BANTER Model
plotDetectorTrace

Plot BANTER Detector Traces
getBanterModel

Extract Random Forest Model
getBanterModelData

Extract Random Forest Model Data
train.data

Training events and detectors