Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Finding The Smoothest Path To Success: Model Complexity And The Consideration Of Nonlinear Patterns In Nest-Survival Data, Max Post Van Der Burg, Larkin A. Powell, Andrew J. Tyre
Finding The Smoothest Path To Success: Model Complexity And The Consideration Of Nonlinear Patterns In Nest-Survival Data, Max Post Van Der Burg, Larkin A. Powell, Andrew J. Tyre
School of Natural Resources: Faculty Publications
Quantifying patterns of nest survival is a first step toward understanding why birds decide when and where to breed. Most studies of nest survival have relied on generalized linear models (GLM) to explore these patterns. However, GLMs require assumptions about the models’ structure that might preclude finding nonlinear patterns in survival data. Generalized additive models (GAM) provide a flexible alternative to GLMs for estimating linear and nonlinear patterns in data. Here we present a comparison of GLMs and GAMs for explaining variation in nest-survival data. We used two different model-selection criteria, the Bayes (BIC) and Akaike (AIC) information criteria, to …
Finding The Smoothest Path To Success: Model Complexity And The Consideration Of Nonlinear Patterns In Nest-Survival Data Encontrando El Camino Mas Facil Hacia El Exito: Complejidad De Los Modelos Y Consideraci6n De Patrones No Lineales En Datos De Supervivencia De Nidos, Max Post Van Der Burg, Larkin A. Powell, Andrew J. Tyre
Finding The Smoothest Path To Success: Model Complexity And The Consideration Of Nonlinear Patterns In Nest-Survival Data Encontrando El Camino Mas Facil Hacia El Exito: Complejidad De Los Modelos Y Consideraci6n De Patrones No Lineales En Datos De Supervivencia De Nidos, Max Post Van Der Burg, Larkin A. Powell, Andrew J. Tyre
School of Natural Resources: Faculty Publications
Quantifying patterns of nest survival is a first step toward understanding why birds decide when and where to breed. Most studies of nest survival have relied on generalized linear models (GLM) to explore these patterns. However, GLMs require assumptions about the models' structure that might preclude finding nonlinear patterns in survival data. Generalized additive models (GAM) provide a flexible alternative to GLMs for estimating linear and nonlinear patterns in data. Here we present a comparison of GLMs and GAMs for explaining variation in nest-survival data. We used two different model-selection criteria, the Bayes (BIC) and Akaike (AIC) information criteria, to …