Data engineering and BI courses are free!
Evaluate whether the detected events are True +, True -, False +, or False - detections by comparing the results to a table of events with known sources and times (such as annotations from viewSpec). Events to evaluate may be either directly from an object of class detectionList, a csv file or data frame resulting from a call to getPeaks or getDetections, or a data frame downloaded from an acoustics database. A value for score.cutoff must be supplied to distinguish between True + and False -, even if assessing all peaks.
eventEval(detections, what = "detections", which.one, standard,
score.cutoff = 11, tol = 1)The detections data frame with an outcome field appended.
An object of class detectionList, a csv file, or data frame containing detection results. See Details.
If a detectionList object is supplied for detections the character value of either "detections" (default; all peaks above the score cutoff) or all "peaks" may be selected.
If the detection process involved multiple templates only one may be selected for evaluation. Value can be either character (identifying the template name), or numerical (identifying the position in names(detections['template']). See Details.
The "standard" is the results from annotation with viewSpec (i.e. Gold Standard) containing the source and time of each event. Can be a data frame or a file path to a csv file.
If no template is supplied a score.cutoff can be supplied to evaluate false negatives.
Numeric value for tolerance, with units seconds. If a detected event is within this value (actually +/- 0.5 x tol), the events are assumed to co-occur and be of the same origin.
Jon Katz
Little checking is performed to ensure that evaluation is possible based on the values for detections and standard. The standard must contain the fields c("start.time", "end.time", "min.frq", "max.frq", "name"). Objects are assumed to be from an acoustics database if they contain the fields c("fldTime", "fldScore", "fldTemplateName"). Data frames are assumed to be objects formerly of class detectionList if they contain the fields c("time", "score", "template").
Results from only one template from one survey may be evaluated in each call to eventEval.
The function timeAlign operates similarly, but rather than evaluate a set of detections against a standard it merges detections from multiple templates and retains only the co-occurring detections with the highest scores.
# Load data
data(btnw)
data(survey)
# Write Wave objects to file (temporary directory used here)
btnw.fp <- file.path(tempdir(), "btnw.wav")
survey.fp <- file.path(tempdir(), "survey2010-12-31_120000_EST.wav")
writeWave(btnw, btnw.fp)
writeWave(survey, survey.fp)
# Make a template
btemp <- makeBinTemplate(btnw.fp, frq.lim = c(2, 8), select = "auto", name = "btnw1", buffer =
4, amp.cutoff = -31, binary = TRUE)
# Binary point matching
scores <- binMatch(survey = survey.fp, templates = btemp, time.source = "fileinfo")
# Isolate peaks
pks <- findPeaks(scores)
# Evaluate peaks
data(survey_anno)
survey_anno <- survey_anno[survey_anno['name'] == 'BTNW', ] # Extract the "BTNW" rows
peaks <- getPeaks(pks)
eval <- eventEval(detections = peaks, standard = survey_anno, score.cutoff = 15)
Run the code above in your browser using DataLab