warbleR (version 1.1.4)

xcorr.graph: Pairwise plots of spectrogram cross-correlation scores

Description

xcorr.graph generates pairwise plots showing the spectrogram cross-correlation scores against the time sliding.

Usage

xcorr.graph(X, cex.cor = 1, cex.lab = 1, cex.axis.lab = 1, rel.cex = FALSE, labs = NULL)

Arguments

X

Output from xcorr function.

cex.cor

A numeric vector of length 1 giving the amount by which correlation scores (in the upper triangle of the multipannel plot) should be magnified. Default is 1.

cex.lab

A numeric vector of length 1 giving the amount by which signal selection labels (in diagonal of the multipannel plot) should be magnified. Default is 1.

cex.axis.lab

A numeric vector of length 1 giving the amount by which the axis labels should be magnified. Default is 1.

rel.cex

Logical. Controls whether the size of the correlation scores (in the upper triangle of the multipannel plot) should be relative to the correlation score.

labs

Alternative selection labels. If not provided the combined name of sound files and selection numbers are used as labels. Default is FALSE.

Details

This function generates pairwise plots of the spectrogram cross-correlation scores by sliding step. The function takes the output of xcorr as input. The colors of the lines in the lower triangle of the plot matrix represent the strenght of the similarity between the two signals. The x axis shows the time difference between the two signals for each sliding step (0 means perfectly centered signals). Note that large number of signals may not display well in the default graphic device. In such cases saving the plot as and image file is adviced.

See Also

xcorr

Examples

## Not run: 
# #load data
# #First set temporal working directory]
# setwd(tempdir())
# 
# #load data
# data(list = c("Phae.long1", "Phae.long2", "manualoc.df"))
# writeWave(Phae.long1, "Phae.long1.wav") #save sound files
# writeWave(Phae.long2, "Phae.long2.wav")
# 
#  #run cross correlation first
#  xcor<-xcorr(X = manualoc.df[1:5,], wl =300, frange= c(2, 9), ovlp=90, dens=0.8, wn='hanning', 
#  cor.method = "pearson") 
#  
#  #plot pairwise scores
#   xcorr.graph(X = xcor, cex.cor = 2, cex.lab = 1, rel.cex = FALSE)
# ## End(Not run)