tag_model_avg: Model Averaging for Instantaneous Rates Tag Return Models

In fishmethods: Fishery Science Methods and Models

Model Averaging for Instantaneous Rates Tag Return Models

Description

Calculates model averaged estimates of instantaneous fishing, natural and total mortality, and survival rates for instantaneous rates tag return models (Hoenig et al. (1998) and Jiang et al. (2007)).

Usage

tag_model_avg(..., global = NULL)

Arguments

...	model object names separated by commas
global	specify global model name in quotes. If the global model is the first model included in the list of candidate models, this argument can be ignored.

Details

Model estimates are generated from functions irm_cr and irm_h. Averaging of model estimates follows the procedures in Burnham and Anderson (2002). Variances of parameters are adjusted for overdispersion using the c-hat estimate from the global model : sqrt(var*c-hat). If c-hat of the global model is <1, then c-hat is set to 1. The c-hat is used to calculate the quasi-likelihood AIC and AICc metrics for each model (see page 69 in Burnham and Anderson(2002)). QAICc differences among models are calculated by subtracting the QAICc of each model from the model with the smallest QAICc value. These differences are used to calculate the Akaike weights for each model following the formula on page 75 of Burnham and Anderson (2002). The Akaike weights are used to calculate the weighted average and standard error of parameter estimates by summing the product of the model-specific Akaike weight and parameter estimate across all models. An unconditional standard error is also calculated by sqrt(sum(QAICc wgt of model i * (var of est of model i + (est of model i - avg of all est)^2))).

Value

List containing model summary statistics, model-averaged estimates of fishing, natural, tag, and total mortality, and survival and their weighted and uncondtional standard errors .

Author(s)

Gary A. Nelson, Massachusetts Division of Marine Fisheries [e-mail]

References

Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference : A Practical Information-Theorectic Approach, 2nd edition. Spriner-Verlag, New York, NY. 488 p.

See Also

irm_h irm_cr

Examples

## This is a typical specification, not a working example
## Not run: 
tag_model_avg(model1,model2,model3,model4,model5,model6,model7,global="model7")
## End(Not run)

fishmethods documentation built on April 27, 2023, 9:10 a.m.