AquaticLifeHistory (version 1.0.6)

Estimate_Len_Maturity: Estimate length-at-maturity

Description

Length-at-maturity is estimated from binary maturity data using a logistic ogive. Two options are available depending on error structure. If binary data are used then a binomial error structure is required. If the user wishes to bin the data by length class then a quasi binomial error structure is needed with the data weighted by the sample size of each bin. This is handled automatically by the function.

Usage

Estimate_Len_Maturity(
  data,
  error.structure = "binomial",
  n.bootstraps = 1000,
  bin.width = NA,
  display.points = FALSE,
  return = "parameters"
)

Value

Either:

parameters

a dataframe of the estimated logistic parameters and their standard error (L50 and L95)

estimates

a dataframe of logistic ogive predictions with 95 percent confidence intervals

plot

a ggplot object of the logistic ogive. If binned length classes are used, this includes a bar plot of proportional maturity

Arguments

data

A dataframe that includes length and a binary maturity status (immature = 0 and mature = 1). Columns should be named "Length" and "Maturity" but the function is robust enough to accept some reasonable variations to these

error.structure

The distribution for the glm used to produce the logistic ogive. Must be either "binomial" for binary data or "quasi binomial" for binned maturity at length. Proportion mature at each length bin is automatically calculated within the function

n.bootstraps

Number of bootstrap iterations required to produce 95% confidence intervals about the logistic ogive

bin.width

The width of the length-class bins used for a quasi binomial logistic model. These should on the same unit as the length data. The y axis on any plots will automatically scale to the correct unit ("cm" or "mm")

display.points

Should the raw data be plotted for the binomial model?

return

Either:

parameters

The estimated logistic parameters and their standard error (L50 and L95)

estimates

The logistic ogive predictions with 95 percent confidence intervals (useful for creating ones own plots)

plot

a ggplot object of the logistic ogive.

Examples

# load example data set
# \donttest{
data("maturity_data")
# Run function to estimate length-at-maturity parameters
Estimate_Len_Maturity(maturity_data)
# A plot can also be returned with bootstrapped CI's. Use 100 bootstraps for
# testing and then increase to at least 1000 for actual model runs.
Estimate_Len_Maturity(maturity_data, return = "plot",n.bootstraps = 100)
# }