summary: BANTER Classifier Model Summary

summaryR Documentation

BANTER Classifier Model Summary

Description

Display summaries for event and detector models

Usage

summary(object, ...)

## S3 method for class 'banter_model'
summary(object, model = "event", n = 0.5, bins = 20, ...)

## S4 method for signature 'banter_model'
summary(object, model = "event", n = 0.5, bins = 20, ...)

Arguments

object

a banter_model object.

...

ignored.

model

name of model to summarize. Default is "event" to summarize the event-level model. Can also be name of a detector.

n

number of final iterations to summarize OOB error rate for. If between 0 and 1 is taken as a proportion of chain.

bins

number of bins in inbag histogram.

Value

In the plot that is created, the upper panel shows the trace of the Random Forest model OOB rate across sequential trees in the forest. The lower plot shows a frequency histogram of the number of times each sample was inbag (used as training data in a tree in the forest). The vertical red lines indicate the expected inbag rate for samples of each species.

Author(s)

Eric Archer eric.archer@noaa.gov

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$events)
# add all detector models
bant.mdl <- addBanterDetector(
  bant.mdl, train.data$detectors, 
  ntree = 50, sampsize = 1, num.cores = 1
)
# run BANTER event model
bant.mdl <- runBanterModel(bant.mdl, ntree = 1000, sampsize = 1)
summary(bant.mdl)


banter documentation built on March 18, 2022, 7:03 p.m.