getlag {MQMF}

getlag is used to look for the response of cpue to previous catches

Description

getlag is a wrapper for the ccf function (cross correlation) that is used within the spm analyses to determine at what negative lag, if any, cpue data is informative about the stock dynamics beyond any information already available in the catch data. If the cpue is directly correlated with catches (lag=0 has a strong correlation) then cpue will not add much more information to an analysis. Only if there is a significant negative correlation is it likely that the cpue will increase the information available and make it more likely that an assessment model may be able to be fitted meaningfully to the available data. If there is no significant negative correlations then it becomes much more unlikely that a useful model fit to the cpue will be possible. The getlag function first finds those rows for which both catch and cpue have values and then it runs the cross-correlation analysis. Thus, you cannot have gaps in your cpue data although there can be catches at the start or end of the time-series, or both, for which there are no cpue data.

Usage

getlag(fish, maxlag = 10, plotout = TRUE, indexI = 1)

Arguments

fish

the matrix or data.frame containing the fishery data (year, catch, and cpue)

maxlag

the lag.max parameter for the ccf function; defaults to 10

plotout

should a plot be made; default=TRUE. If FALSE then, assuming the result of the analysis is put into an object called 'ans' a call to plot(ans) will generate the required plot.

indexI

if there are more than one time-series of cpue/indices then this parameter selects which to use

Value

an object of class acf, which can be plotted

Examples

year <- 1985:2008
catch <- c(1018,742,868,715,585,532,566,611,548,499,479,428,657,481,
           645,961,940,912,955,935,940,952,1030,985)
cpue <- c(0.6008,0.6583,0.6791,0.6889,0.7134,0.7221,0.7602,0.7931,0.8582,
          0.8876,1.0126,1.1533,1.2326,1.2764,1.3307,1.3538,1.2648,1.2510,
          1.2069,1.1552,1.1238,1.1281,1.1113,1.0377)
dat <- as.data.frame(cbind(year,catch,cpue))
out <- getlag(dat,plotout=FALSE)
plot(out,lwd=3,col=2)
str(out)