
compare.methods
creates graphs to visually assess performance of acoustic distance measurements
compare.methods(X = NULL, flim = c(0, 22), bp = c(0, 22), mar = 0.1, wl = 512, ovlp = 90,
res = 150, n = 10, length.out = 30, methods = c("XCORR", "dfDTW", "ffDTW", "SP"),
it = "jpeg", parallel = 1, path = NULL, sp = NULL, pb = TRUE, grid = TRUE,
clip.edges = TRUE, threshold = 15, na.rm = FALSE, scale = FALSE,
pal = reverse.gray.colors.2, img = TRUE, ...)
A numeric vector of length 2 for the frequency limit in kHz of
the spectrogram, as in spectro
. Default is c(0, 22).
numeric vector of length 2 giving the lower and upper limits of the
frequency bandpass filter (in kHz) used in the acoustic distance methods. Default is c(0, 22). Note that
for XCORR this argument sets the frange argument from the xcorr
function.
Numeric vector of length 1. Specifies plot margins around selection in seconds. Default is 0.1.
A numeric vector of length 1 specifying the window length of the spectrogram and cross-correlation, default is 512.
Numeric vector of length 1 specifying the percent overlap between two
consecutive windows, as in spectro
. Default is 90.
Numeric argument of length 1. Controls image resolution. Default is 150.
Numeric argument of length 1. Defines the number of plots to be produce. Default is 10.
A character vector of length 1 giving the number of measurements of fundamental or dominant frequency desired (the length of the time series). Default is 30.
A character vector of length 2 giving the names of the acoustic distance
methods that would be compared. The methods available are: cross-correlation (XCORR, from
xcorr
), dynamic time warping on dominant frequency time series (dfDTW, from
dtw
applied on dfts
output), dynamic time warping on dominant
frequency time series (ffDTW, from dtw
applied on ffts
output),
spectral parameters (SP, from specan
).
A character vector of length 1 giving the image type to be used. Currently only "tiff" and "jpeg" are admitted. Default is "jpeg".
Numeric. Controls whether parallel computing is applied. It specifies the number of cores to be used. Default is 1 (i.e. no parallel computing). Not available in Windows OS.
Character string containing the directory path where the sound files are located.
If NULL
(default) then the current working directory is used.
Data frame with acoustic parameters as the one generated by specan
. Must contain 'sound.files'
and "selec' columns and the same selections as in 'X'.
Logical argument to control progress bar. Default is TRUE
. Note that progress bar is only used
when parallel = 1.
Logical argument to control the presence of a grid on the spectrograms (default is TRUE
).
Logical argument to control whether edges (start or end of signal) in
which amplitude values above the threshold were not detected will be removed when using dfDTW and
ffDTW methods. If TRUE
this edges will be excluded and signal contour will be calculated on the
remainging values. Default is TRUE
.
amplitude threshold (%) for dominant and/or fundamental frequency detection when using dfDTW, ffDTW and SP methods. Default is 15.
Logical. If TRUE
all NAs produced when pairwise cross-correlations failed are removed from the
results. This means that all selections with at least 1 cross-correlation that failed are excluded in both methods under
comparison. Only apply if XCORR is one of the methods being compared.
Logical. If TRUE
dominant and/or fundamental frequency values are z-transformed using the scale
function, which "ignores" differences in absolute frequencies between the signals in order to focus the
comparison in the frequency contour, regardless of the pitch of signals. Default is TRUE
.
A color palette function to be used to assign colors in the
spectrograms, as in spectro
. Default is reverse.gray.colors.2.
A logical argument specifying whether an image files would be produced. Default is TRUE
.
Additional arguments to be passed to a modified version of spectro
for customizing
graphical output. This includes fast.spec, an argument that speeds up the plotting of spectrograms (see description in
specreator
).
Image files with 4 spectrograms of the selection being compared and scatterplots of the acoustic space of all signals in the input data frame 'X'.
This function produces graphs with spectrograms from 4 signals in the
provided data frame that allow visual inspection of the performance of acoustic
distance methods at comparing those signals. The signals are randomly picked up
from the provided data frame (X argument).The spectrograms are all plotted with
the same frequency and time scales. The function compares 2 methods at a time. The
methods available are: cross-correlation
(XCORR, from xcorr
), dynamic time warping on dominant frequency time
series (dfDTW, from dtw
applied on dfts
output), dynamic time
warping on dominant frequency time series (ffDTW, from dtw
applied on
ffts
output), spectral parameters (SP, from specan
). The graph also
contains 2 scatterplots (1 for each method) of the acoustic space of all signals in the
input data frame 'X'. The compared selections are randomly picked up from the pool of
selections in the input data frame. The argument 'n' defines the number of comparisons (i.e. graphs)
to be produced. The acoustic pairwise distance between signals is shown next
to the arrows linking them. The font color of a distance value correspond to the font
color of the method that generated it, as shown in the scatterplots. Distances are
standardized, being 0 the distance of a signal to itself and 1 the farthest pairwise
distance in the pool of signals. Principal Component Analysis (princomp
)
is applied to calculate distances when using spectral parameters (SP). In that case the first 2 PC's are used. Classical
Multidimensional Scalling (also known as Principal Coordinates Analysis,
(cmdscale
)) is used for all other methods. Note that SP can only be used with at least 22 selections (number of rows in input data frame) as PCA only works with more units than variables. The graphs are return as image files in the
working directory. The file name contains the methods being compared and the
rownumber of the selections. This function uses internally a modified version
of the spectro
function from seewave package to create spectrograms.
# NOT RUN {
{
# Set temporary working directory
setwd(tempdir())
data(list = c("Phae.long1", "Phae.long2", "Phae.long3", "Phae.long4", "selec.table"))
writeWave(Phae.long1,"Phae.long1.wav")
writeWave(Phae.long2,"Phae.long2.wav")
writeWave(Phae.long3,"Phae.long3.wav")
writeWave(Phae.long4,"Phae.long4.wav")
compare.methods(X = selec.table, flim = c(0, 10), bp = c(0, 10), mar = 0.1, wl = 300,
ovlp = 90, res = 200, n = 10, length.out = 30,
methods = c("XCORR", "dfDTW"), parallel = 1, it = "jpeg")
#remove progress bar
compare.methods(X = selec.table, flim = c(0, 10), bp = c(0, 10), mar = 0.1, wl = 300,
ovlp = 90, res = 200, n = 10, length.out = 30,
methods = c("XCORR", "dfDTW"), parallel = 1, it = "jpeg", pb = FALSE)
#check this folder!
getwd()
#compare SP and XCORR
#first we need to create a larger data set as the PCA that summarizes the spectral parameters
#needs more units (rows) that variables (columns)
#so I just create a new selection table repeating 3 times selec.table
st2 <- rbind(selec.table, selec.table, selec.table)
#note that the selection labels should be also changed
st2$selec <- 1:nrow(st2)
#now we can compare SP method against XCORR
compare.methods(X = st2, flim = c(0, 10), bp = c(0, 10), mar = 0.1, wl = 300,
ovlp = 90, res = 200, n = 10, length.out = 30,
methods = c("XCORR", "SP"), parallel = 1, it = "jpeg")
#compare SP method against dfDTW
compare.methods(X = st2, flim = c(0, 10), bp = c(0, 10), mar = 0.1, wl = 300,
ovlp = 90, res = 200, n = 10, length.out = 30,
methods = c("dfDTW", "SP"), parallel = 1, it = "jpeg")
#alternatively we can provide our own SP matrix
sp <- specan(selec.table, bp = c(0, 10))
#and selec just a few variables to avoid the problem of # observations vs # parameters in PCA
sp <- sp[, 1:7]
compare.methods(X = selec.table, flim = c(0, 10), sp = sp, bp = c(0, 10), mar = 0.1, wl = 300,
ovlp = 90, res = 200, n = 10, length.out = 30,
methods = c("XCORR", "SP"), parallel = 1, it = "jpeg")
#note that "SP" should also be included as a method in 'methods'
#again, all images are saved in the working directory
}
# }
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