Age-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 age 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.
Estimate_Age_Maturity(
data,
error.structure = "binomial",
n.bootstraps = 1000,
display.points = FALSE,
return = "parameters"
)A dataframe that includes age and a binary maturity status (immature = 0 and mature = 1). Columns should be named "Age" and "Maturity" but the function is robust enough to accept some reasonable variations to these
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 age. Proportion mature at each age is automatically calculated within the function
Number of bootstrap iterations required to produce 95% confidence intervals about the logistic ogive
Should the raw data be plotted for the binomial model?
Either:
The estimated logistic parameters and their standard error (A50 and A95)
The logistic ogive predictions with 95 percent confidence intervals (useful for creating ones own plots)
a ggplot object of the logistic ogive.
Either:
a dataframe of the estimated logistic parameters and their standard error (A50 and A95)
a dataframe of logistic ogive predictions with 95 percent confidence intervals
a ggplot object of the logistic ogive