Inference in generalized linear mixed models (GLMM) remains a topic of debate.
Baayen, Davidson, and Bates (2008) outlines criticism against conventional ways of
performing inference for GLMMs. There are various alternatives proposed but lit-
tle consistency is found on which is the most reasonable. Our focus is on assessing
temporal trends for mainly ecological count data. That is, we hope to provide a prag-
matic approach to Poisson GLMMs for ecological researchers within the statistical
programming environment R. To achieve this, we start by providing a description of
the selected estimation and inferential procedures. We then complete a large scale
simulation to evaluate each of the estimation methods. We implement a power analy-
sis to assess each of the selected inferential procedures. We then go on to apply these
procedures to data sampled by The National Parks of Canada. Finally, we conclude by giving a summary of our ?ndings and outlying work for the future.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/13184 |
| Date | 13 December 2010 |
| Creators | Reddick, Edward |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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