ICtab.mod.select: Compute AIC/AICc Table and Select Best Model

In gdauby/ConR: Computation of Parameters Used in Preliminary Assessment of Species Conservation Status

Compute AIC/AICc Table and Select Best Model

Description

Compute the values of AIC and AICc, compare them and return the model comparison, the model with best support from the data and its name.

Usage

ICtab.mod.select(x, correct = 40, cutoff = log(8), parsimony = TRUE)

Arguments

x	a named list with the models to be compared
correct	numerical. The value of number of observations divided by the number of model parameters to apply the correction to the AIC
cutoff	numerical. The threshold value of delta-AIC to select equally plausible models
parsimony	logical. Should the most parsimonious model be select in case of two or more equally plausible models? Default to TRUE.

Details

This function was adapted from the functions AICtab and AICctab from package bbmle (Bolker 2017), and should provide the same outputs. The main difference of ICtab.mod.select is that it some alternatives for allowing the comparison of models with few or more degrees of freedom then the number of observations, which return infinite values of logLik of the correction term in the AICc comparison. These alternatives are statistically incorrect, but they allow model ranking for selection within the needs of package ConR. These alternatives are provided because the assessment of species conservation status often relies on very few observations (e.g. population size estimates).

For the process of selecting the best model, we followed some basic steps. By default, if two or more models had delta-AIC smaller than the cutoff provided, the more parsimonious model (i.e. the model with less parameters) is selected. However, this decision can be changed (i.e. select the model with best fit) by setting the argument 'parsimony' to FALSE. Next, if more than one model is selected (i.e. both have the same number of parameters), the selection process give preference to models that are not linear or quadratic, which tend provide projections that reach zero much faster and that can generate non-realistic projections depending on the population data or on the years chosen for the projection period.

Value

a list with the model selection table and the best model.

Author(s)

Renato A. Ferreira de Lima

References

D. Anderson and K. Burnham (2004). Model selection and multi-model inference. Second edition. Springer-Verlag, New York.

Ben Bolker and R Development Core Team (2017). bbmle: Tools for General Maximum Likelihood Estimation. R package version 1.0.20. https://CRAN.R-project.org/package=bbmle

gdauby/ConR documentation built on Jan. 30, 2024, 11:10 p.m.