Calculate a weighted average of model estimates (e.g. coefficients, fitted values, residuals) for a set of models.
avgEst(est, weights = "equal", est.names = NULL, ...)A list or nested list of numeric vectors, comprising the model estimates. In the latter case, these should correspond to estimates for candidate models for each of a set of different response variables.
An optional numeric vector of weights to use for model
averaging, or a named list of such vectors. The former should be supplied
when est is a list, and the latter when it is a nested list (with
matching list names). If weights = "equal" (default), a simple
average is calculated instead.
An optional vector of names used to extract and/or sort estimates from the output.
Not currently used.
A numeric vector of the model-averaged estimates, or a list of such vectors.
This function can be used to calculate a weighted average of model estimates such as coefficients, fitted values, or residuals, where models are typically competing candidate models fit to the same response variable. Weights are typically a 'weight of evidence' type metric such as Akaike model weights (Burnham & Anderson 2002, Burnham et al. 2011), which can be conveniently calculated in R using packages such as MuMIn or AICcmodavg. However, numeric weights of any sort can be used. If none are supplied, the simple average is calculated instead.
Averaging is performed via the 'full'/'zero' rather than 'subset'/'conditional'/'natural' method, meaning that zero is substituted for estimates for any 'missing' parameters (e.g. coefficients) prior to calculations. This provides a form of shrinkage and thus reduces estimate bias (Burnham & Anderson 2002, Grueber et al. 2011).
Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York: Springer-Verlag. Retrieved from https://www.springer.com/gb/book/9780387953649
Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011). AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology and Sociobiology, 65(1), 23-35. https://doi.org/c4mrns
Dormann, C. F., Calabrese, J. M., Guillera-Arroita, G., Matechou, E., Bahn, V., Barton, K., ... Hartig, F. (2018). Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs, 88(4), 485-504. https://doi.org/gfgwrv
Grueber, C. E., Nakagawa, S., Laws, R. J., & Jamieson, I. G. (2011). Multimodel inference in ecology and evolution: challenges and solutions. Journal of Evolutionary Biology, 24(4), 699-711. https://doi.org/b7b5d4
Walker, J. A. (2019). Model-averaged regression coefficients have a straightforward interpretation using causal conditioning. BioRxiv, 133785. https://doi.org/c8zt
# NOT RUN {
## Model-averaged coefficients
m <- Shipley.Growth # candidate models
b <- lapply(m, function(i) coef(summary(i))[, 1])
avgEst(b)
## Using weights
w <- runif(length(b), 0, 1)
avgEst(b, w)
## Model-averaged predictions
f <- lapply(m, predict)
avgEst(f, w)
# }
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