banter (version 0.9.6)

summary: BANTER Classifier Model Summary

Description

Display summaries for event and detector models

Usage

summary(object, ...)
# S3 method for banter_model
summary(object, model = "event", n = 0.5, bins = 20, ...)
# S4 method for banter_model
summary(object, model = "event", n = 0.5, bins = 20, ...)

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.

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.

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