Learn R Programming

icesAdvice (version 2.1.1)

DLS3.2: DLS Method 3.2

Description

Apply ICES method 3.2 to calculate catch advice for data-limited stocks (DLS).

Usage

DLS3.2(lastadvice, index, len = c(3, 2), buffer = FALSE, i1, i2)

Arguments

lastadvice

last catch advice given for this stock.

index

stock size index.

len

two integers, indicating the desired lengths of reference vectors.

buffer

whether to apply a -20% precautionary buffer.

i1

included for backward compatibility, use len instead.

i2

included for backward compatibility, use len instead.

Value

A list containing the resulting advice and other elements showing intermediate steps in the calculations.

Details

This function compares the average values of two reference vectors i1 and i2. In the simplest case, only lastadvice and index are required to calculate the advice.

The default value of len = c(3, 2) produces vectors i1 and i2 of lengths 3 and 2,

i1 = (I[n-4], I[n-3], I[n-2])

i2 = (I[n-1], I[n])

where I is a stock size index of length n.

Other vector lengths can be used, such as len = c(5, 2) to get

i1 = (I[n-6], I[n-5], I[n-4], I[n-3], I[n-2])

i2 = (I[n-1], I[n])

Finally, a -20% precautionary buffer can be applied at the end of all calculations.

See the ICES (2012) guidance report for details.

References

ICES (2012) ICES DLS guidance report: ICES implementation of advice for data-limited stocks in 2012 in its 2012 advice. ICES CM 2012/ACOM:68.

See Also

read.dls and write.dls read and write DLS3.2 results to file.

icesAdvice-package gives an overview of the package.

Examples

Run this code
# NOT RUN {
# Three hypothetical surveys
survey <- data.frame(year=2001:2010, randu[1:10,])

DLS3.2(1000, survey$x)

DLS3.2(1000, survey$y)
DLS3.2(1000, survey$y, len=c(5,2))

DLS3.2(1000, survey$z)
DLS3.2(1000, survey$z, buffer=TRUE)

# Plot
output <- DLS3.2(1000, survey$y)
plot(y~year, survey, ylab="index", type="b", lty=3)
segments(2006, output$i1bar, 2008, lwd=2)
segments(2009, output$i2bar, 2010, lwd=2)

# }

Run the code above in your browser using DataLab