Nomograms aid interpretation of complex regression models

Robert A. Gitzen, Joshua J. Millspaugh

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Ecologists often develop complex regression models that include multiple categorical and continuous variables, interactions among predictors, and nonlinear relationships between the response and predictor variables. Nomograms, which are graphical devices for presenting mathematical functions and calculating output values, can aid biologists in interpreting and presenting these complex models. To illustrate benefits of nomograms, we developed a logistic regression model of elk (Cervus elaphus) resource selection. With this model, we demonstrated how a nomogram helps scientists and managers interpret interactions among variables, compare the relative biological importance of variables, and examine predicted shapes of relationships (e.g., linear vs. nonlinear) between response and predictor variables. Although our example focused on logistic regression, nomograms are equally useful for other linear and nonlinear models. Regardless of the approach used for model development, nomograms and other graphical summaries can help scientists and managers develop, interpret, and apply statistical models.

Original languageEnglish
Pages (from-to)2438-2443
Number of pages6
JournalJournal of Wildlife Management
Issue number7
StatePublished - Sep 2007


  • Habitat modeling
  • Linear model
  • Logistic regression
  • Nomogram
  • Resource selection


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