Estimate_Growth {AquaticLifeHistory} | R Documentation |

## Estimate length-at-age parameters and growth curves for Elasmobranchs

### Description

A multi-model growth estimation approach is applied to length-at-age data. Three models can be applied which include the von Bertalanffy (VB), logistic (Log) and Gompertz (Gom) models. AIC values and weights are calculated. The outputs will return a list of model parameter estimates and will either print a plot to the screen or output the length-at-age estimates as part of the list.Use of this function should cite Smart et al. (2016).

### Usage

```
Estimate_Growth(
data,
models = c("VB", "Log", "Gom"),
Birth.Len = NULL,
correlation.matrix = FALSE,
n.bootstraps = 1000,
plots = TRUE,
Max.Age = NULL,
plot.legend = TRUE
)
```

### Arguments

`data` |
a data frame which includes 'Age' and 'Length - ideally with these names but the function will except some variation to these |

`models` |
a vector of models to be fitted. These can include" VB", "Log" and "Gom". A subset can also be used |

`Birth.Len` |
The length-at-birth to be used for two parameter models. If a value is provided, two parameter models are automatically run |

`correlation.matrix` |
Should the correlation matrix of parameters be returned? This is the only object returned if TRUE. |

`n.bootstraps` |
The number of bootstraps performed for model 95 confidence intervals |

`plots` |
Should plots be printed to the screen. If FALSE then the model estimates and CI's are returned as an additional output |

`Max.Age` |
Specify the max age for bootstrapped confidence intervals to be produced over. Default is the max age in the data. |

`plot.legend` |
Do you want a legend for the different models on the plot |

### Value

Returns a list of parameter estimates with errors and AIC results. If plots is TRUE then a plot is printed to the screen. If plots is FALSE then the length-at-age estimates are returned as a list element

### References

Smart et al. (2016) Multi-model approaches in shark and ray growth studies: strengths, weaknesses and the future. Fish and Fisheries. 17: 955-971https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12154

### Examples

```
# load example data set
data("growth_data")
# Run function with three default model candidates. Use 100 bootstraps for
# testing and then increase to at least 1000 for actual model runs.
Estimate_Growth(growth_data, n.bootstraps = 100)
```

*AquaticLifeHistory*version 1.0.5 Index]