corspec {seewave} | R Documentation |
This function tests the similarity between two frequency spectra by returning their maximal correlation and the frequency shift related to it.
corspec(x, y, range, plot = TRUE, plotval = TRUE, method = "spearman", col = "black", colval = "red", cexval = 1, fontval = 1, xlab = "Frequency (kHz)", ylab = "Coefficient of correlation (r)", ...)
x |
a first data set resulting of a spectral analysis obtained
with spec or meanspec (not in dB). |
y |
a second data set resulting of a spectral analysis obtained
with spec or meanspec (not in dB). |
range |
range of x and y (in kHz). |
plot |
logical, if TRUE plots r values against frequency shift (by default TRUE ). |
plotval |
logical, if TRUE adds to the plot maximum r value
and frequency offset (by default TRUE ). |
method |
a character string indicating which correlation coefficient is
to be computed ("pearson", "spearman", or "kendall")
(see cor ). |
col |
colour of r values. |
colval |
colour of r max and frequency offset values. |
cexval |
character size of r max and frequency offset values. |
fontval |
font of r max and frequency offset values. |
xlab |
title of the frequency axis. |
ylab |
title of the r axis. |
... |
other plot graphical parameters. |
It is important not to have data in dB.
Successive correlations between x
and y
are computed when regularly
shifting y
towards lower or higher frequencies.
The maximal correlation is obtained at a particular shift (frequency offset).
This shift may be positive or negative.
The corresponding p value, obtained with cor.test
, is plotted.
Inverting x
and y
may give slight different results, see examples.
If plot
is FALSE
, corspec
returns a list containing four
components:
r |
the successive correlation values between x and y . |
rmax |
the maximum correlation value between x and y . |
p |
the p value corresponding to rmax . |
f |
the frequency offset corresponding to rmax . |
Jérôme Sueur jerome.sueur@ibaic.u-psud.fr
Hopp, S. L., Owren, M. J. and Evans, C. S. (Eds) 1998. Animal acoustic communication. Springer, Berlin, Heidelberg.
spec
, meanspec
, corspec
,
covspectro
, cor
, cor.test
.
data(tico) # compare the two first notes spectra a<-spec(tico,f=22050,wl=512,at=0.2,plot=FALSE) c<-spec(tico,f=22050,wl=512,at=1.1,plot=FALSE) op<-par(mfrow=c(2,1), mar=c(4.5,4,3,1)) spec(tico,f=22050,wl=512,at=0.2,col="blue",type="l") par(new=TRUE) spec(tico,f=22050,wl=512,at=1.1,col="green",type="l") legend(x=8,y=0.5,c("Note A", "Note C"),lty=1,col=c("blue","green"),bty="o") par(mar=c(5,4,2,1)) corspec(a,c,range=c(0,11.025),type="l",ylim=c(-0.25,0.8),xaxs="i",yaxs="i",las=1) par(op) # different correlation methods give different results... op<-par(mfrow=c(3,1)) corspec(a,c,range=c(0,11.025),type="l",xaxs="i",las=1, ylim=c(-0.25,0.8)) title("spearmann correlation (by default)") corspec(a,c,range=c(0,11.025),type="l",xaxs="i",las=1,ylim=c(0,1),method="pearson") title("pearson correlation") corspec(a,c,range=c(0,11.025),type="l",xaxs="i",las=1,ylim=c(-0.23,0.5),method="kendall") title("kendall correlation") par(op) # inverting x and y does not give exactly similar results op<-par(mfrow=c(2,1),mar=c(2,4,3,1)) corspec(a,c,range=c(0,11.025),type="l") corspec(c,a,range=c(0,11.025),type="l") par(op)