Calculate von Bertanlaffy Growth Parameters from Lengths and Ages

Calculate von Bertanlaffy growth parameters from length and age data or predicted lengths given ages and input parameters.

Usage

estgrowth.vb(Par, Ages, Lengths, ReturnType = c("NLL", "Pred"), sdFactor = 1)

Arguments

Par: A list of von Bertanlaffy growth parameters in log space ordered as follows: K, Linf, L0, CV0, and CV1. Names will be assigned if they are not provided.
Ages: A vector of ages in years. Values of NA are accepted.
Lengths: A vector of Lengths in cm. Lengths can be NULL if ReturnType == "Pred" because you are only predicting using ages, where the lengths are just needed for estimation purposes. If not NULL, ensure that there is one length measurement for every age measurement. Values of NA are accepted.
ReturnType: A single character value with "NLL" being the default, which leads to the negative log-likelihood value being returned. If "Pred", then three values are returned for each combination of length and age, low, prediction, and high based on the input parameters and standard deviation factor, i.e., sdFactor.
sdFactor: The number of standard deviations to include in the low and high calculations. The default is 1.0.

Value

Depending on ReturnType, either the negative log likelihood is returned based on fits to the data or a matrix of three columns with low, predicted, and high values for each combination of length and age. Distance of the low and high from the predicted value depends on the sdFactor, allowing confidence intervals based on normal theory or other theories to be created.

Examples

if (FALSE) { # \dontrun{
bio_dat <- data.frame(
  Age = rep(0:30, each = 20),
  Length_cm = rnorm(n = 31 * 20, mean = 50, sd = 5)
)
pars_in <- lapply(FUN = log, X = list(
  "K" = 0.13,
  "Linf" = 55,
  "L0" = 5,
  "CV0" = 0.1,
  "CV1" = 0.1
))
solve <- optim(
  fn = estgrowth.vb, par = unlist(pars_in), hessian = FALSE,
  Ages = bio_dat[, "Age"],
  Lengths = bio_dat[, "Length_cm"]
)
predictions <- estgrowth.vb(
  Par = solve$par, ReturnType = "Pred",
  sdFactor = 1,
  Ages = bio_dat[, "Age"],
  Lengths = bio_dat[, "Length_cm"]
)
plot(bio_dat$Age, predictions[, "Lhat_pred"],
  xlab = "Age (years)", ylab = "Predicted length (cm)"
)
exp(solve$par)
} # }