transitionM: Transition Matrix

In ejosymart/Gtransition: Estimate a Stochastic Growth Matrix Based on Length Structure Data

Transition Matrix

Description

Estimates the probability of each individual growing from one length class to another over a time-step G_{l, l+1} based on two probabilistic density functions: gamma and normal distributions.

Usage

transitionM(
  lowerL,
  upperL,
  classL,
  distribution = "gamma",
  delta,
  beta = NULL,
  sigma = NULL
)

Arguments

lowerL	a numeric value that represents the smallest observed size.
upperL	a numeric value that represents the highest observed size.
classL	a numeric value that represents the range length classes.
distribution	a character string defining the growth equation to be used. One of "gamma" or "normal".
delta	a numeric vector that represents mean growth increments \bar Δ_l. The output of function mgi().
beta	a numeric value that represents the shape parameter of gamma distribution density function. Required when "gamma" distribution is selected.
sigma	a numeric value the represents the variability of normal distribution density function. Required when "normal" distribution is selected.

Details

The probabilistic density function defines the probability region where individuals may grow considering the probability that the increment in length does not occur and the individuals remain in their original length class (Haddon, 2011).

Value

A list of class 'Mtransition'.

mcdf a matrix that contain the probabilities of growth increments.

G a matrix that contain the expected proportion of individuals growing from class l to length class l +1.

References

Luquin-Covarrubias, M.A. and E. Morales-Bojórquez. 2021. Effects of stochastic growth on population dynamics and management quantities estimated from an integrated catch-at-length assessment model: Panopea globosa as case study. Ecological Modelling. 440: 109384. https://doi.org/10.1016/j.ecolmodel.2020.109384.

Examples

output <- mgi(lowerL = 78, upperL = 202, classL = 4, Linf = 197.42, k = 0.1938, method = "vonB")
delta <- output$delta

mat <- transitionM(lowerL = 78, upperL = 202, classL = 4, distribution = "gamma", 
delta = delta, beta = 0.105, sigma = NULL)

mat

ejosymart/Gtransition documentation built on Feb. 11, 2023, 2:29 a.m.