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README.Rmd
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README.Rmd
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---
output:
md_document:
variant: gfm
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
library(Gtransition)
```
# Gtransition
**Estimate a Stochastic Growth Matrix Based on Length Structure Data**
This package describes a theoretical model expressing the variability observed in the individual growth, such that each individual in the population exhibits a growth pattern with a nonlinear trend toward an expected value. Thus, the growth is represented by the proportion of individuals in the length class $l$ during a time interval. The proportion of individuals that grow from length class $l$ to all length classes $l^{'}$ is represented by a probabilistic density function, usually gamma distribution or normal distribution; therefore the growth pattern depends of their parameters, where the mean value indicates the average growth increment, and the variance explains the individual variability in growth, consequently both parameters determine the proportion of individuals going from one length class to another.
## Installation
Install the CRAN version:
```R
install.packages("Gtransition")
```
Or install de development version:
```R
# install.packages("devtools")
devtools::install_github("ejosymart/Gtransition")
```
After, that call the package:
```R
library("Gtransition")
```
## Examples
This is a basic example which shows you how to calculate the transition growth matrix:
## Mean growth increment (based on von Bertalanffy equation)
```{r}
output <- mgi(lowerL = 78, upperL = 202, classL = 4,
Linf = 197.42, k = 0.1938, method = "vonB")
output
delta <- output$delta
Laverage <- output$Laverage
```
## Transition growth matrix
```{r}
Gmat <- transitionM(lowerL = 78, upperL = 202, classL = 4,
distribution = "gamma",
delta = delta, beta = 0.105, sigma = NULL)
```
# Plots
```{r}
plot(Gmat)
```
```{r}
plot(Gmat, xlab = "XLAB", ylab = "YLAB", adjY = -25,
col = "grey40", sizeAxis1 = 0.5, sizeAxis2 = 0.5,
filename = "myplot",
savePDF = TRUE, widthPDF = 3, heightPDF = 10,
savePNG = TRUE, widthPNG = 300, heightPNG = 1000, resPNG = 110)
```
### References
Luquin-Covarrubias M., Morales-Bojorquez E. (2020). Effects of stochastic growth on population dynamics and management quantities estimated from an integrated catch-at-length assessment model: *Panopea globosa* as case study. Ecologial Modeling 440, 109384. https://doi.org/10.1016/j.ecolmodel.2020.109384
Sullivan P.J., Lai H., Galluci V.F. (1990). A Catch-at-Length analysis that incorporates a stochastic model of growth. Can. J. Fish. Aquat. Sci. 47: 184-198.