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Table 3 Model selection results for the generalized linear mixed effects models of the NIP

From: Masting by beech trees predicts the risk of Lyme disease

Rank Model structure df logLik AIC ΔAIC Weight1 Weight2
1 NIP ~ Y + RLB + PR 5 − 2363.1 4736.2 0.0 47.0 47.0
2 NIP ~ Y + RLB + PR + S 8 − 2360.1 4736.2 0.0 47.0 94.0
3 NIP ~ Y + RLB + RH1 5 − 2365.4 4740.8 4.6 5.0 99.0
  1. Model selection results are shown for the generalized linear mixed effects models (GLMMs) with binomial errors of the NIP response variable. The explanatory variables were site, year, beech masting index 2 years prior, DIN from the previous year, RLB time lag, and the climate variables obtained from the weather stations and the field. The models are ranked according to their Akaike information criterion (AIC). Of the 232 models in the set, only the 3 top models are shown for which the cumulative support (Weight2) > 99.0%. Shown for each model are the model rank (Rank), model structure (see Table 1 for the acronyms of the explanatory variables), model degrees of freedom (df), log-likelihood (logLik), AIC, difference in the AIC value from the top model (ΔAIC), model weight (Weight1), and cumulative model weight (Weight2). The results from the full model selection are shown in Additional file 1: Section 5