The Flea Poem Analysis - TES Resources
The Flea Analysis John Donne Characters archetypes
We used Generalised Linear Mixed Models (GLMM) with a logit link, binomial errors and fitted using REML to investigate what factors influence the probability that an individual field vole was infested with fleas (using presence or absence of fleas as a binary response variable). Similar analyses were conducted for each of the Bartonella species. The large number of explanatory variables to be considered resulted in many potential models. Moreover, due to the study design, infection prevalences could be spatially correlated. Consequently, we conducted the analyses in several stages (detailed fully below). For the Bartonella analyses the investigation of fixed effects comprised 3 stages: the first investigated population level covariates that were shared by all individuals trapped in a clearcut during a single survey, the second investigated individual level covariates, and the third investigated the impact of fleas on infection probabilities. The flea analysis excluded the third stage. After modelling the fixed effects we examined whether the residual extra-binomial variation in the data exhibited spatial structuring. Care was taken to check the robustness of results with and without random effects. Three nested random effects were included in each analysis: survey, forest within a survey (forest*survey) and site within a survey (clearcut*survey). Prior to considering fixed effects, we determined what portion of the total variance each of the random effects explained, and whether each of the variance components was significantly different from zero using z-tests in the SAS GLIMMIX procedure ().
The flea analysis excluded the third stage
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