gillnetfit: Millar's original gillnet selectivity fitting function

Description

Function to estimate selectivity parameters from experimental data. This function is applied within select_Millar to derive starting parameters. select_Millar is the recommended function for selectivity estimation.

Usage

gillnetfit(
  data,
  meshsizes,
  rtype = "norm.loc",
  rel.power = NULL,
  plotlens = NULL,
  details = FALSE
)

Arguments

data

matrix with the number of individuals caught with each sized mesh (CatchPerNet_mat).

meshsizes

vector with meshSizes in increasing order (meshSizes),

rtype

A character string indicating which method for estimating selection curves should be used: "norm.loc" for a normal curve with common spread, "norm.sca" for a normal curve with variable spread, "lognorm" for a lognormal curve, "gamma" for a gamma curve.

rel.power

A string indicating the relative power of different meshSizes, must have same length as meshSizes (Default: rel.power = NULL).

plotlens

lengths which should be used for graphical output, for more detailed curves. Default : NULL

details

logical; should details be included in the output?

Value

list of fitted parameters

References

Millar, R. B., Holst, R., 1997. Estimation of gillnet and hook selectivity using log-linear models. ICES Journal of Marine Science: Journal du Conseil, 54(3):471-477

Examples

Run this code

# NOT RUN {
data(gillnet)

dat <- matrix(c(gillnet$midLengths, gillnet$CatchPerNet_mat),
         byrow = FALSE, ncol=(dim(gillnet$CatchPerNet_mat)[2]+1))

gillnetfit(data = dat, meshsizes = gillnet$meshSizes)


# }

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