Journal Article

Model averaging and muddled multimodel inferences

Cade, Brian S

2015

Overview

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse ( Centrocercus urophasianus ) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

• Publisher: Ecological Society of America
• Subject: Animal Distribution / Animals / biogeography / Centrocercus urophasianus / Colorado / Ecological modeling / Ecology / Galliformes - physiology / generalized linear models / Grade point average / Greater Sage-Grouse / Mathematical independent variables / Mathematics / model averaging / model uncertainty / Modeling / Models, Biological / multicollinearity / multimodel inference / Natural resources / natural resources conservation / Parametric models / partial effects / partial standard deviations / prediction / Predictions / Regression analysis / Regression coefficients / relative importance of predictors / Resource conservation / species distribution models / Standard deviation / Standardized tests / Statistics / Uncertainty / Variables / variance / zero-truncated Poisson regression