Millar's selectivity model
Description
This model estimates the selecitvity of different gears from experimental catches.
Usage
1 2  select_Millar(param, x0 = NULL, rtype = "norm.loc", rel.power = NULL,
plot = TRUE)

Arguments
param 
A list with following parameters: vector with midlengths of size classes
( 
x0 
A string of initial values for the parameters to be optimized over when applying the
function 
rtype 
A character string indicating which method for estimating selection curves
should be used:

rel.power 
A string indicating the relative power of different meshSizes,
must have same length as 
plot 
logical; should a plot be printed? 
Details
Model adapted from the selectivity functions provided by Prof. Dr. Russell Millar
(https://www.stat.auckland.ac.nz/~millar/). In the deviance plot open circles correspond to negative,
closed to positive residuals. The size of the circles is proportional to the square of the residuals.
To assess the model fit by the deviance plot it requires some experience, in general the pattern should
be random and the sizes not too big. Please refer to Millar's publications and other publications for
comparison. The model can produce errors if the starting values (x0
) for the optim
function are not realistic. Please be aware that if the method is changed the outcoming parameters
can greatly vary. Simliarly the starting values have to be adapted when changing the method (rtype
).
Source
https://www.stat.auckland.ac.nz/~millar/selectware/
References
Millar, R. B., Holst, R., 1997. Estimation of gillnet and hook selectivity using loglinear models. ICES Journal of Marine Science: Journal du Conseil, 54(3):471477
Holt, S. J. 1963. A method for determining gear selectivity and its application. ICNAF Special Publication, 5: 106115.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44  data(haddock)
output < select_Millar(haddock, x0 = c(10,0.3,0),
rtype = "tt.logistic")
plot(output, plotlens=seq(25,35,0.1), deviance_plot = FALSE)
legend("topleft",c("Control","Experimental"), lty=1:2, col=1:2)
# Gillnet
data(gillnet)
# Using inital estimates from old method
select_Millar(gillnet, x0 = NULL, rtype = "norm.loc")$value
select_Millar(gillnet, x0 = NULL, rtype = "norm.sca")$value
select_Millar(gillnet, x0 = NULL, rtype = "lognorm")$value
# Calculation with finer length resolution
output < select_Millar(gillnet, x0 = NULL, rtype = "lognorm")
plot(output, plotlens=seq(40,90,0.1))
# Use alternate plot settings
output < select_Millar(gillnet, x0 = NULL, rtype = "lognorm")
ncolor < length(output$meshSizes)
plot(output, plotlens=seq(40,90,0.1), deviance_plot = FALSE,
lty=1, col=rainbow(ncolor))
legend("topleft", col=rainbow(ncolor), legend=output$meshSizes,
lty=1, title="Mesh size [cm]")
# deviance plot only
plot(output, plotlens=seq(40,90,0.1), selectivity_plot = FALSE)
# Stacked trammel net
# The data come from two experiments using different mesh sizes
# This analysis assumes common retention curve in both experiments.
# Note that summary function does not produce residual plot
# since lengths are not unique
data(trammelnet)
output < select_Millar(trammelnet, x0 = c(25,4),
rtype="norm.loc", rel.power = rep(1,6))
plot(output,plotlens=seq(10,40,0.1))
