summary | R Documentation |
Display summaries for event and detector models
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, ...)
object |
a |
... |
ignored. |
model |
name of model to summarize. Default is |
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. |
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.
Eric Archer eric.archer@noaa.gov
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
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)
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