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We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015–2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons ('exuviae') and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples. [ABSTRACT FROM AUTHOR]

Background: In response to an unexpected long cold spell in February 1996 which killed more than 100 older adults (mostly living alone) in Hong Kong, the Hong Kong Senior Citizen Home Safety Association established a Personal Emergency Link Service to provide emergency contact to the older adults, which uses a telephone system to render emergency relief and total care service around the clock. To facilitate the dynamic and efficient allocation of service resources, it is crucial to understand the factors linked with use of the services and number of hospital admissions arising from PE link service.Methods: We initially use the Poisson generalized linear model (GLM) with polynomial effect functions of relevant covariates. If the time series of residuals from fitting the Poisson GLM reveals significant serial correlation, a Poisson generalized linear autoregressive moving average (GLARMA) model is refitted to the data to account for the auto-correlation among the time series of daily call numbers. If the data is overdispersed relative to the best fitting Poisson GLARMA model, then the negative binomial GLARMA model is refitted to account for any overdispersion. In all the models, dummy variables for weekdays and months are included to account for any cyclic trends due weekday effect or month of the year effect. The secular time trend is modeled by a polynomial function of calendar time over the study period. Finally any critical temperatures are identified by visually inspecting the graph of the effect function of temperature.Results: The weekday and month effects are both significant with Monday seeing more PE Link calls than Sunday and June seeing less than January. Temperature has significant effect on the PE Link call rate with the effect highly nonlinear. A critical temperature, below which excessive increase in PE link calls that lead to hospital admissions, is identified to be around 15 °C.Conclusion: Identifying a threshold temperature which generates an excessive increase in the expected number of PE Link calls would be useful in service provision planning and support for elderly in need of hospital admission. [ABSTRACT FROM AUTHOR]

Response surface methodology (RSM) is a collection of techniques useful for analyzing and optimizing problems where several explanatory covariates influence a response. Although this technique is extensively used in various mixture experiments, its application in standardization of micropropagation protocols is limited. The theoretical developments of RSM are usually concerned with continuous data; hence, linear model theory becomes relevant. In plant tissue culture, in which the response variables are mostly numerical data, the development of RSM in a generalized linear model (GLM) setup is of interest from both a theoretical as well as an application perspective. In the present paper, RSM, as applicable for count data, has been used for modeling, analyzing, and optimizing in vitro regeneration of multiple shoots of Basilicum polystachyon, an important medicinal plant. The specific issues addressed herein are the determination of the optimum concentration of plant growth regulators ( i.e., the range of variation in dosages of each covariate) at which the regeneration potential of shoot tip explants is expected to increase, selection of the appropriate growth function (response function) of shoot tip, and determination of the optimum levels of the explanatory variables ( i.e., the different combination of dosages of various control factors) for experimentation. According to the present analysis, the optimum level combinations of growth regulators for regeneration of multiple shoots from shoot tip explants of B. polystachyon is 8.19 μM benzyladenine and 2.36 μM naphthalene acetic acid, with a response of approximately 12 regenerated shoots. [ABSTRACT FROM AUTHOR]