This session will be held in the Erskine Building, Room 445
14:15 — 14:35
University of Otago
Queensland University of Technology
In many ecological research studies, abundance data are skewed and contain more zeros than might be expected. Often, the aim is to model abundance in terms of covariates, and to estimate expected abundance for a given set of covariate values. Welsh et al. (1996) have advocated use of a conditional-model approach for this purpose. This allows one to separately model presence and abundance given presence, which should lead to a more complete understanding as to how the covariates influence abundance. The focus of this talk is on the calculation of confidence intervals for expected abundance given particular values of the covariates. The Wald confidence interval used by Welsh et al. (1996) is symmetric, and therefore unlikely to be of much use for skewed data, where confidence intervals for abundance measures are likely to be asymmetric. We show how to calculate a profile likelihood confidence interval for expected abundance using a conditional model.
14:35 — 14:55
University of Auckland
C. M. Triggs
University of Auckland
J. A. D. Anderson
New Zealand Institute for Crop & Food Research Limited
World wide, potato (Solanum tuberosum L.) is considered one of the most important vegetable crops. Late blight caused by Phytophthora infestans is recognized as the most serious potato disease. A biennial field screening trial for resistance to late blight has been carried out at Pukekohe for over twenty years. Trials were laid out as latinised row and column designs in a single rectangular array of plots, indexed by rows and columns. In each trial disease severity based on the percentage of affected foliage was repeatedly assessed on a 1-9 ordinal scale from the first sign of infection in each trial and at 4 to 6 subsequent occasions.
Based on threshold model for ordinal responses, we developed a Bayesian nonlinear model which fits a logistic sigmoidal decay curve to the latent variable for repeated ordinal measurements and random effects arisen from latinised row and column design.
14:55 — 15:15
We present an analysis of datasets consisting of start/stop times of individual deer feeding episodes over several days of continuous automated observation. Feeding episodes occur in clusters which constitute 'meals', during which the deer is primarily feeding, while at other times it is engaged in some other activity. Individuals within the group tend to have their meals at the same time. Since activity is not observed, but only whether or not each animal is feeding, this suggests that a hidden Markov or semi-Markov model could be used to analyse the data. Such models for individual cattle, but with no group context, have been used by Allcroft et al (2004). We also consider a generalization including feedback given by Zucchini et al (2005).