Home About us Contact
|
Home About us Contact | ||
|
|
||
Generalized Linear Mixed Models (generalized + linear_mixed_models)
Selected AbstractsStress-induced dynamic adjustments of reproduction differentially affect fitness components of a semi-arid plantJOURNAL OF ECOLOGY, Issue 1 2008Cristina F. Aragón Summary 1Summer drought stress is considered the primary constraint to plant performance in Mediterranean ecosystems. However, little is known about the implications of summer stress for plant reproduction under real field conditions and, particularly, for the regulatory mechanisms of maternal investment in reproduction. 2The relationship between plant physiological status at different reproductive stages over the course of the summer drought period and final reproductive output was modelled in the Mediterranean semi-arid specialist Helianthemum squamatum. 3Plant physiological status, assessed by the chlorophyll fluorescence-based parameter Fv/Fm, and soil moisture content beneath each plant, were determined in the field at five key phenological moments in a total of 88 plants. We used Generalized Linear Mixed Models to evaluate the effect of plant physiological status at those different dates on several components of reproduction (number of flowers and seeds per plant, fruit-set and intra-fruit seed abortion). We included soil moisture as an additional predictor to statistically control its potential effect on reproduction. 4Fv/Fm measured at midday was a significant predictor of reproductive output, but its significance varied over time and with the specific reproductive response variable. Fv/Fm measured at the onset of flowering was positively related to the number of flowers and seeds per plant, whereas Fv/Fm at the fruiting peak positively affected fruit-set. Soil moisture content was only significant when measured before flowering, being positively related to total flowers and seeds. The effect of stress on reproductive output acted either at an early stage of the reproductive season, by varying the number of flowers produced and seed primordia initiated, or at a later stage, by adjusting the number or ripe fruits. 5Synthesis. Our results show a direct relationship between physiological status and reproduction, and highlight the importance of the timing of stress for reproductive success. They also show that small departures from the physiological optimum at specific reproductive stages may cause significant decreases in the reproductive output. We suggest that the dynamic adjustment of reproduction in response to stress is adaptive in fluctuating and unpredictable Mediterranean semi-arid environments, where an adequate temporal distribution of maternal resources determines the species' ability to withstand severe environmental conditions. [source] Cluster Detection Based on Spatial Associations and Iterated Residuals in Generalized Linear Mixed ModelsBIOMETRICS, Issue 2 2009Tonglin Zhang Summary Spatial clustering is commonly modeled by a Bayesian method under the framework of generalized linear mixed effect models (GLMMs). Spatial clusters are commonly detected by a frequentist method through hypothesis testing. In this article, we provide a frequentist method for assessing spatial properties of GLMMs. We propose a strategy that detects spatial clusters through parameter estimates of spatial associations, and assesses spatial aspects of model improvement through iterated residuals. Simulations and a case study show that the proposed method is able to consistently and efficiently detect the locations and magnitudes of spatial clusters. [source] Diagnosis of Random-Effect Model Misspecification in Generalized Linear Mixed Models for Binary ResponseBIOMETRICS, Issue 2 2009Xianzheng Huang Summary Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihood-based inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for random-effect model misspecification in GLMMs for clustered binary response. We provide a theoretical justification of the proposed method and investigate its finite sample performance via simulation. The proposed method is applied to data from a longitudinal respiratory infection study. [source] Bayesian Covariance Selection in Generalized Linear Mixed ModelsBIOMETRICS, Issue 2 2006Bo Cai Summary The generalized linear mixed model (GLMM), which extends the generalized linear model (GLM) to incorporate random effects characterizing heterogeneity among subjects, is widely used in analyzing correlated and longitudinal data. Although there is often interest in identifying the subset of predictors that have random effects, random effects selection can be challenging, particularly when outcome distributions are nonnormal. This article proposes a fully Bayesian approach to the problem of simultaneous selection of fixed and random effects in GLMMs. Integrating out the random effects induces a covariance structure on the multivariate outcome data, and an important problem that we also consider is that of covariance selection. Our approach relies on variable selection-type mixture priors for the components in a special Cholesky decomposition of the random effects covariance. A stochastic search MCMC algorithm is developed, which relies on Gibbs sampling, with Taylor series expansions used to approximate intractable integrals. Simulated data examples are presented for different exponential family distributions, and the approach is applied to discrete survival data from a time-to-pregnancy study. [source] Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed ModelsBIOMETRICS, Issue 2 2002Ole F. Christensen Summary. Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction. [source] On Estimation and Prediction for Spatial Generalized Linear Mixed ModelsBIOMETRICS, Issue 1 2002Hao Zhang Summary. We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. We develop a Monte Carlo version of the EM gradient algorithm for maximum likelihood estimation of model parameters. A by-product of this approach is that it also produces the MMSE estimates for the realized random effects at the sampled sites. This method is illustrated through a simulation study and is also applied to a real data set on plant root diseases to obtain a map of disease severity that can facilitate the practice of precision agriculture. [source] Factors influencing territorial occupancy and reproductive output in the Booted Eagle Hieraaetus pennatusIBIS, Issue 4 2006JOSÉ E. MARTÍNEZ During a 7-year research project in a forested area of southeastern Spain, we studied territorial occupancy and reproductive success in a Booted Eagle Hieraaetus pennatus population. We monitored 65 territories, gathering information on 406 occupancy events and 229 breeding attempts, including those of two potential competitors, the Northern Goshawk Accipiter gentilis and the Common Buzzard Buteo buteo. Generalized linear mixed models were used to explain occupancy and productivity, by evaluating the relative contribution of three different types of variables (habitat, competition and past events) and considering territory as a random effect. We examined a set of a priori hypothesized models, together with a number of additional models, and selected the best models following an information-theoretic approach. Our best models related territorial occupancy and productivity to previous breeding success (the fledging of one or two young), which appeared to be the most important factor determining the probability of reoccupation and the reproductive output in the subsequent year. The best occupation model revealed that the probabilities of occupancy were also conditioned by a competition variable (intraspecific nearest-neighbour distance) and two habitat variables (the location of the nest on the valley slope and the distance to the nearest forest track). Unlike the best occupation model, however, the selected model for reproductive output did not incorporate any competition variable besides previous breeding success, but included another two habitat variables (the effects of trunk height and NNE orientation). [source] Diagnosis of Random-Effect Model Misspecification in Generalized Linear Mixed Models for Binary ResponseBIOMETRICS, Issue 2 2009Xianzheng Huang Summary Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihood-based inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for random-effect model misspecification in GLMMs for clustered binary response. We provide a theoretical justification of the proposed method and investigate its finite sample performance via simulation. The proposed method is applied to data from a longitudinal respiratory infection study. [source] Latitudinal patterns in the phenological responses of leaf colouring and leaf fall to climate change in JapanGLOBAL ECOLOGY, Issue 4 2008Hideyuki Doi ABSTRACT Aim, To estimate the potential effect of global climate change on the phenological responses of plants it is necessary to estimate spatial variations at larger scales. However, previous studies have not estimated latitudinal patterns in the phenological response directly. We hypothesized that the phenological response of plants varies with latitude, and estimated the phenological response to long-term climate change using autumn phenological events that have been delayed by recent climate change. Location, Japan. Methods, We used a 53-year data set to document the latitudinal patterns in the climate responses of the timing of autumn leaf colouring and fall for two tree species over a wide range of latitudes in Japan (31 to 44° N). We calculated single regression slopes for leaf phenological timing and air temperature across Japan and tested their latitudinal patterns using regression models. The effects of latitude, time and their interaction on the responses of the phenological timings were also estimated using generalized linear mixed models. Results, Our results showed that single regression slopes of leaf phenological timing and air temperature in autumn were positive at most stations. Higher temperatures can delay the timing of leaf phenology. Negative relationships were found between the phenological response of leaves to temperature and latitude. Single regression slopes of the phenological responses at lower latitudes were larger than those at higher latitudes. Main conclusions, We found negative relationships between leaf phenological responsiveness and latitude. These findings will be important for predicting phenological timing with global climate change. [source] On quasi-likelihood inference in generalized linear mixed models with two components of dispersionTHE CANADIAN JOURNAL OF STATISTICS, Issue 4 2003Brajendra C. Sutradhar Abstract The authors propose a quasi-likelihood approach analogous to two-way analysis of variance for the estimation of the parameters of generalized linear mixed models with two components of dispersion. They discuss both the asymptotic and small-sample behaviour of their estimators, and illustrate their use with salamander mating data. Les auteurs s'inspirent de l'analyse de la variance à deux voies pour proposer une méthode d'estimation de type quasi-vraisemblance des paramètres de modèles linéaires généralisés mixtes ayant deux composantes de dispersion. Es étudient le comportement asymptotique et à taille finie de leurs estimateurs et en illustrent l'emploi au moyen de données portant sur l'accouplement de salamandres. [source] Pregnancy and Birth After Kidney Donation: The Norwegian ExperienceAMERICAN JOURNAL OF TRANSPLANTATION, Issue 4 2009A. V. Reisæter Reports on pregnancies in kidney donors are scarce. The aim was to assess pregnancy outcomes for previous donors nationwide. The Medical Birth Registry of Norway holds records of births since 1967. Linkage with the Norwegian Renal Registry provided data on pregnancies of kidney donors 1967,2002. A random sample from the Medical Birth Registry was control group, as was pregnancies in kidney donors prior to donation. Differences between groups were assessed by two-sided Fisher's exact tests and with generalized linear mixed models (GLMM). We identified 326 donors with 726 pregnancies, 106 after donation. In unadjusted analysis (Fisher) no differences were observed in the occurrence of preeclampsia (p = 0.22). In the adjusted analysis (GLMM) it was more common in pregnancies after donation, 6/106 (5.7%), than in pregnancies before donation 16/620 (2.6%) (p = 0.026). The occurrence of stillbirths after donation was 3/106 (2.8%), before donation 7/620 (1.1%), in controls (1.1%) (p = 0.17). No differences were observed in the occurrence of adverse pregnancy outcome in kidney donors and in the general population in unadjusted analysis. Our finding of more frequent preeclampsia in pregnancies after kidney donation in the secondary analysis must be interpreted with caution, as the number of events was low. [source] Modeling dependencies between rating categories and their effects on prediction in a credit risk portfolioAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2008Claudia Czado Abstract The internal-rating-based Basel II approach increases the need for the development of more realistic default probability models. In this paper, we follow the approach taken in McNeil A and Wendin J 7 (J. Empirical Finance 2007) by constructing generalized linear mixed models for estimating default probabilities from annual data on companies with different credit ratings. The models considered, in contrast to McNeil A and Wendin J 7 (J. Empirical Finance 2007), allow parsimonious parametric models to capture simultaneously dependencies of the default probabilities on time and credit ratings. Macro-economic variables can also be included. Estimation of all model parameters are facilitated with a Bayesian approach using Markov chain Monte Carlo methods. Special emphasis is given to the investigation of predictive capabilities of the models considered. In particular, predictable model specifications are used. The empirical study using default data from Standard and Poor's gives evidence that the correlation between credit ratings further apart decreases and is higher than the one induced by the autoregressive time dynamics. Copyright © 2008 John Wiley & Sons, Ltd. [source] Marginal Mark Regression Analysis of Recurrent Marked Point Process DataBIOMETRICS, Issue 2 2009Benjamin French Summary Longitudinal studies typically collect information on the timing of key clinical events and on specific characteristics that describe those events. Random variables that measure qualitative or quantitative aspects associated with the occurrence of an event are known as marks. Recurrent marked point process data consist of possibly recurrent events, with the mark (and possibly exposure) measured if and only if an event occurs. Analysis choices depend on which aspect of the data is of primary scientific interest. First, factors that influence the occurrence or timing of the event may be characterized using recurrent event analysis methods. Second, if there is more than one event per subject, then the association between exposure and the mark may be quantified using repeated measures regression methods. We detail assumptions required of any time-dependent exposure process and the event time process to ensure that linear or generalized linear mixed models and generalized estimating equations provide valid estimates. We provide theoretical and empirical evidence that if these conditions are not satisfied, then an independence estimating equation should be used for consistent estimation of association. We conclude with the recommendation that analysts carefully explore both the exposure and event time processes prior to implementing a repeated measures analysis of recurrent marked point process data. [source] Variances Are Not Always Nuisance ParametersBIOMETRICS, Issue 2 2003Raymond J. Carroll Summary In classical problems, e.g., comparing two populations, fitting a regression surface, etc., variability is a nuisance parameter. The term "nuisance parameter" is meant here in both the technical and the practical sense. However, there are many instances where understanding the structure of variability is just as central as understanding the mean structure. The purpose of this article is to review a few of these problems. I focus in particular on two issues: (a) the determination of the validity of an assay; and (b) the issue of the power for detecting health effects from nutrient intakes when the latter are measured by food frequency questionnaires. I will also briefly mention the problems of variance structure in generalized linear mixed models, robust parameter design in quality technology, and the signal in microarrays. In these and other problems, treating variance structure as a nuisance instead of a central part of the modeling effort not only leads to inefficient estimation of means, but also to misleading conclusions. [source] On Estimation and Prediction for Spatial Generalized Linear Mixed ModelsBIOMETRICS, Issue 1 2002Hao Zhang Summary. We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. We develop a Monte Carlo version of the EM gradient algorithm for maximum likelihood estimation of model parameters. A by-product of this approach is that it also produces the MMSE estimates for the realized random effects at the sampled sites. This method is illustrated through a simulation study and is also applied to a real data set on plant root diseases to obtain a map of disease severity that can facilitate the practice of precision agriculture. [source] When Should Epidemiologic Regressions Use Random Coefficients?BIOMETRICS, Issue 3 2000Sander Greenland Summary. Regression models with random coefficients arise naturally in both frequentist and Bayesian approaches to estimation problems. They are becoming widely available in standard computer packages under the headings of generalized linear mixed models, hierarchical models, and multilevel models. I here argue that such models offer a more scientifically defensible framework for epidemiologic analysis than the fixed-effects models now prevalent in epidemiology. The argument invokes an antiparsimony principle attributed to L. J. Savage, which is that models should be rich enough to reflect the complexity of the relations under study. It also invokes the countervailing principle that you cannot estimate anything if you try to estimate everything (often used to justify parsimony). Regression with random coefficients offers a rational compromise between these principles as well as an alternative to analyses based on standard variable-selection algorithms and their attendant distortion of uncertainty assessments. These points are illustrated with an analysis of data on diet, nutrition, and breast cancer. [source] | |||||||||||||