gillnetfit function

Millar's original gillnet selectivity fitting function

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.

Source

https://www.stat.auckland.ac.nz/~millar/selectware/

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?

Returns

list of fitted parameters

Examples

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)

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