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Garrido, José, and Jun Zhou. "Full Credibility with Generalized Linear and Mixed Models." ASTIN Bulletin 39, no. 1 (May 2009): 61–80. http://dx.doi.org/10.2143/ast.39.1.2038056.
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AbstractGeneralized linear models (GLMs) are gaining popularity as a statistical analysis method for insurance data. For segmented portfolios, as in car insurance, the question of credibility arises naturally; how many observations are needed in a risk class before the GLM estimators can be considered credible? In this paper we study the limited fluctuations credibility of the GLM estimators as well as in the extended case of generalized linear mixed model (GLMMs). We show how credibility depends on the sample size, the distribution of covariates and the link function. This provides a mechanism to obtain confidence intervals for the GLM and GLMM estimators.
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Fox, Jean-Paul, Duco Veen, and Konrad Klotzke. "Generalized Linear Mixed Models for Randomized Responses." Methodology 15, no. 1 (January 1, 2019): 1–18. http://dx.doi.org/10.1027/1614-2241/a000153.
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Abstract. Response bias (nonresponse and social desirability bias) is one of the main concerns when asking sensitive questions about behavior and attitudes. Self-reports on sensitive issues as in health research (e.g., drug and alcohol abuse), and social and behavioral sciences (e.g., attitudes against refugees, academic cheating) can be expected to be subject to considerable misreporting. To diminish misreporting on self-reports, indirect questioning techniques have been proposed such as the randomized response techniques. The randomized response techniques avoid a direct link between individual’s response and the sensitive question, thereby protecting the individual’s privacy. Next to the development of the innovative data collection methods, methodological advances have been made to enable a multivariate analysis to relate responses to sensitive questions to other variables. It is shown that the developments can be represented by a general response probability model (including all common designs) by extending it to a generalized linear model (GLM) or a generalized linear mixed model (GLMM). The general methodology is based on modifying common link functions to relate a linear predictor to the randomized response. This approach makes it possible to use existing software for GLMs and GLMMs to model randomized response data. The R-package GLMMRR makes the advanced methodology available to applied researchers. The extended models and software will seriously improve the application of the randomized response methodology. Three empirical examples are given to illustrate the methods.
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Hayati, Ma'rufah, and Agus Muslim. "Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling." Enthusiastic : International Journal of Applied Statistics and Data Science 1, no. 01 (April 24, 2021): 36–52. http://dx.doi.org/10.20885/enthusiastic.vol1.iss1.art6.
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Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate data of the General Circulation Model (GCM) output in the form of precipitation. However, the application of SDS modeling is known to cause several problems, including correlated and not stationary response variables, multi-dimensional explanatory variables, multicollinearity, and spatial correlation between grids. Modeling with some of these problems will cause violations of the assumptions of independence and multicollinearity. This research aims to model the rainfall in Indramayu Regency, West Java Province using a combined regression model between the Generalized linear mixed model (GLMM) and Least Absolute Selection and Shrinkage Operator (LASSO) regulation (L1). GLMM was used to deal with the problem of independence and Lasso Regulation (L1) was used to deal with multicollinearity problems or the number of explanatory variables that is greater than the response variable. Several models were formed to find the best model for modeling rainfall. This research used the GLMM-Lasso model with Normal spread compared to the GLMM model with Gamma response (Gamma-GLMM). The results showed that the RMSE and R-square GLMM-Lasso models were smaller than the Gamma-GLMM models. Thus, it can be concluded that GLMM-Lasso model can be used to model statistical downscaling and solve the previously mentioned constraints. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021
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Zhu, Rui, Chao Jiang, Xiaofeng Wang, Shuang Wang, Hao Zheng, and Haixu Tang. "Privacy-preserving construction of generalized linear mixed model for biomedical computation." Bioinformatics 36, Supplement_1 (July 1, 2020): i128—i135. http://dx.doi.org/10.1093/bioinformatics/btaa478.
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Abstract Motivation The generalized linear mixed model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes random effects into account. Given its power of precisely modeling the mixed effects from multiple sources of random variations, the method has been widely used in biomedical computation, for instance in the genome-wide association studies (GWASs) that aim to detect genetic variance significantly associated with phenotypes such as human diseases. Collaborative GWAS on large cohorts of patients across multiple institutions is often impeded by the privacy concerns of sharing personal genomic and other health data. To address such concerns, we present in this paper a privacy-preserving Expectation–Maximization (EM) algorithm to build GLMM collaboratively when input data are distributed to multiple participating parties and cannot be transferred to a central server. We assume that the data are horizontally partitioned among participating parties: i.e. each party holds a subset of records (including observational values of fixed effect variables and their corresponding outcome), and for all records, the outcome is regulated by the same set of known fixed effects and random effects. Results Our collaborative EM algorithm is mathematically equivalent to the original EM algorithm commonly used in GLMM construction. The algorithm also runs efficiently when tested on simulated and real human genomic data, and thus can be practically used for privacy-preserving GLMM construction. We implemented the algorithm for collaborative GLMM (cGLMM) construction in R. The data communication was implemented using the rsocket package. Availability and implementation The software is released in open source at https://github.com/huthvincent/cGLMM. Supplementary information Supplementary data are available at Bioinformatics online.
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Zhong, Yuan, Baoxin Hu, G. Brent Hall, Farah Hoque, Wei Xu, and Xin Gao. "A Generalized Linear Mixed Model Approach to Assess Emerald Ash Borer Diffusion." ISPRS International Journal of Geo-Information 9, no. 7 (June 27, 2020): 414. http://dx.doi.org/10.3390/ijgi9070414.
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The Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis Fairmaire) can cause damage to all species of Ash trees (Fraxinus), and rampant, unchecked infestations of this insect can cause significant damage to forests. It is thus critical to assess and model the spread of the EAB in a manner that allows authorities to anticipate likely areas of future tree infestation. In this study, a generalized linear mixed model (GLMM), combining the features of the commonly used generalized linear model (GLM) and a random effects model, was developed to predict future EAB spread patterns in Southern Ontario, Canada. The GLMM was designed to deal with autocorrelation in the data. Two random effects were established based on the geographic information provided with the EAB data, and a method based on statistical inference was proposed to identify the most significant factors associated with the distribution of the EAB. The results of the model showed that 95% of the testing data were correctly classified. The predictive performance of the GLMM was substantially enhanced in comparison with that obtained by the GLM. The influence of climatic factors, such as wind speed and anthropogenic activities, had the most significant influence on the spread of the EAB.
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Fortin, Mathieu. "Population-averaged predictions with generalized linear mixed-effects models in forestry: an estimator based on Gauss−Hermite quadrature." Canadian Journal of Forest Research 43, no. 2 (February 2013): 129–38. http://dx.doi.org/10.1139/cjfr-2012-0268.
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Data in forestry are often spatially and (or) serially correlated. In the last two decades, mixed models have become increasingly popular for the analysis of such data because they can relax the assumption of independent observations. However, when the relationship between the response variable and the covariates is nonlinear, as is the case in generalized linear mixed models (GLMMs), population-averaged predictions cannot be obtained from the fixed effects alone. This study proposes an estimator, which is based on a five-point Gauss−Hermite quadrature, for population-averaged predictions in the context of GLMM. The estimator was tested through Monte Carlo simulation and compared with a regular generalized linear model (GLM). The estimator was also applied to a real-world case study, a harvest model. The results showed that GLM predictions were unbiased but that their confidence intervals did not achieve their nominal coverage. On the other hand, the proposed estimator yielded unbiased predictions with reliable confidence intervals. The predictions based on the fixed effects of a GLMM exhibited the largest biases. If statistical inferences are needed, the proposed estimator should be used. It is easily implemented as long as the random effect specification does not contain multiple random effects for the same hierarchical level.
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MAIORANO, Amanda Marchi, Thiago Santos MOTA, Ana Carolina VERDUGO, Ricardo Antonio da Silva FARIA, Beatriz Pressi Molina da SILVA, Márcia Cristina de Sena OLIVEIRA, Joslaine Noely dos Santos Gonçalves CYRILLO, and Josineudson Augusto II de Vasconcelos SILVA. "COMPARATIVE STUDY OF CATTLE TICK RESISTANCE USING GENERALIZED LINEAR MIXED MODELS." REVISTA BRASILEIRA DE BIOMETRIA 37, no. 1 (March 25, 2019): 41. http://dx.doi.org/10.28951/rbb.v37i1.341.
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Comparison of tick resistance in Bos taurus indicus (Nelore) and Bos taurus taurus (Simmental and Caracu) subspecies was investigated utilizing generalized linear mixed models (GLMMs) with Poisson and Negative binomial distributions. Nelore animals (NE) are known to present greater resistance than t. taurus. Difference between tick resistance in Simmental (SI) and Caracu (CA) breeds has never been reported previously. Three artificial tick infestations were conducted to evaluate tick resistance in these breeds. The statistic point of the present study was to show alternative models for the evaluation of tick count data, the GLMMs. Analysis for tick resistance by GLMM with Negative binomial distribution has never been assessed previously. The analyses were performed by the use of the PROC GLIMMIX procedure of the SAS program. The results showed that GLMM with Negative binomial distribution is appropriated to evaluate tick count data with excess of zero observations avoiding overdispersion problems. Finally, considering multiple comparisons with the Bonferroni test, different pattern of tick infestation was observed for the studied breeds, suggesting that NE is the most resistant breed followed by CA.
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Dietz, L. R., and S. Chatterjee. "Logit-normal mixed model for Indian monsoon precipitation." Nonlinear Processes in Geophysics 21, no. 5 (September 12, 2014): 939–53. http://dx.doi.org/10.5194/npg-21-939-2014.
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Abstract. Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Four GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data. The logit-normal model was applied to light, moderate, and extreme rainfall. Findings indicated that physical constructs were preserved by the models, and random effects were significant in many cases. We also found GLMM estimation methods were sensitive to tuning parameters and assumptions and therefore, recommend use of multiple methods in applications. This work provides a novel use of GLMM and promotes its addition to the gamut of tools for analysis in studying climate phenomena.
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Islam, Tahmidul, Md Golam Rabbani, and Wasimul Bari. "Analyzing Child Malnutrition in Bangladesh: Generalized Linear Mixed Model Approach." Dhaka University Journal of Science 64, no. 2 (July 31, 2016): 163–67. http://dx.doi.org/10.3329/dujs.v64i2.54492.
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Child malnutrition is a serious issue for overall child health and future development. Stunting is a key anthropometric indicator of child malnutrition. Because of the nature of sampling design used in Bangladesh Demographic Health Survey, 2011, responses obtained from children under same family might be correlated. Again, children residing in same cluster may also be correlated. To tackle this problem, generalized linear mixed model (GLMM), instead of usual fixed effect logistic regression model, has been utilized in this paper to find out potential factors affecting child malnutrition. Model performances have also been compared.
Dhaka Univ. J. Sci. 64(2): 163-167, 2016 (July)
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Koper, Nicola, and Micheline Manseau. "A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models." Rangifer 32, no. 2 (March 8, 2012): 195. http://dx.doi.org/10.7557/2.32.2.2269.
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Resource selection functions (RSF) are often developed using satellite (ARGOS) or Global Positioning System (GPS) telemetry datasets, which provide a large amount of highly correlated data. We discuss and compare the use of generalized linear mixed-effects models (GLMM) and generalized estimating equations (GEE) for using this type of data to develop RSFs. GLMMs directly model differences among caribou, while GEEs depend on an adjustment of the standard error to compensate for correlation of data points within individuals. Empirical standard errors, rather than model-based standard errors, must be used with either GLMMs or GEEs when developing RSFs. There are several important differences between these approaches; in particular, GLMMs are best for producing parameter estimates that predict how management might influence individuals, while GEEs are best for predicting how management might influence populations. As the interpretation, value, and statistical significance of both types of parameter estimates differ, it is important that users select the appropriate analytical method. We also outline the use of k-fold cross validation to assess fit of these models. Both GLMMs and GEEs hold promise for developing RSFs as long as they are used appropriately.
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Rohmaniah, Siti Alfiatur, and Novita Eka Chandra. "PERHITUNGAN PREMI ASURANSI JIWA MENGGUNAKAN GENERALIZED LINEAR MIXED MODELS." Jurnal Ilmiah Teknosains 4, no. 2 (January 3, 2019): 80. http://dx.doi.org/10.26877/jitek.v4i2.3004.
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The price of life insurance premiums for each person depends on the probability of death, not only based on age and gender as offered by an Indonesian insurance company. The purpose of this study is to determine premium prices on underwriting factors and frailty factors using Generalized Linear Mixed Models (GLMM). GLMM is used for modeling a combination of fixed effect heterogeneity (underwriting factors) and random effects (frailty factors) between individuals. The data used longitudinal data about underwriting factors that have Binomial distribution are taken from the Health and Retirement Study and processed using R software. Because the data used by survey data within an interval of two years, so the probability of death is obtained for an interval the next two years. Underwriting factors that have a significant effect on the probability of death are age, alcoholic status, heart disease, and diabetes. As a result, is obtained the probability of death models each individual to determine life insurance premium prices. The premium price of each individual is different because depends on underwriting factors and frailty. If frailty is positive, it means that a person level of vulnerability when experiencing the risk of death is greater than negative frailty.
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Dietz, L. R., and S. Chatterjee. "Logit-normal mixed model for Indian Monsoon rainfall extremes." Nonlinear Processes in Geophysics Discussions 1, no. 1 (March 13, 2014): 193–233. http://dx.doi.org/10.5194/npgd-1-193-2014.
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Abstract. Describing the nature and variability of Indian monsoon rainfall extremes is a topic of much debate in the current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Several GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data procured from the National Climatic Data Center. The logit-normal model was applied with fixed covariates of latitude, longitude, elevation, daily minimum and maximum temperatures with a random intercept by weather station. In general, the estimation methods concurred in their suggestion of a relationship between the El Niño Southern Oscillation (ENSO) and extreme rainfall variability estimates. This work provides a valuable starting point for extending GLMM to incorporate the intricate dependencies in extreme climate events.
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Tovissodé, Chénangnon Frédéric, Aliou Diop, and Romain Glèlè Kakaï. "Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm." PLOS ONE 16, no. 4 (April 6, 2021): e0249604. http://dx.doi.org/10.1371/journal.pone.0249604.
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Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions. Interestingly, both the traditional logistic and probit mixed models, as well as other available methods can be utilized within the skew generalized t-link model (SGTLM) frame. We have taken advantage of the Expectation-Maximization algorithm accelerated via parameter-expansion for model fitting. We evaluated the performance of this approach to GLMMs through a simulation experiment by varying sample size and data distribution. Our findings indicated that the proposed methodology outperforms competing approaches in estimating population parameters and predicting random effects, when the traditional link and normality assumptions are violated. In addition, empirical standard errors and information criteria proved useful for detecting spurious skewness and avoiding complex models for probit data. An application with respiratory infection data points out to the superiority of the SGTLM which turns to be the most adequate model. In future, studies should focus on integrating the demonstrated flexibility in other generalized linear mixed models to enhance robust modeling.
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Madden, L. V., W. W. Turechek, and M. Nita. "Evaluation of Generalized Linear Mixed Models for Analyzing Disease Incidence Data Obtained in Designed Experiments." Plant Disease 86, no. 3 (March 2002): 316–25. http://dx.doi.org/10.1094/pdis.2002.86.3.316.
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Diseased individuals (e.g., leaves, plants) typically are clustered in nature, resulting in greater heterogeneity or variability of disease incidence than would be expected for a random pattern. To account for this variability, as well as the binary nature of disease incidence and the multiple sources of variation in designed experiments, a generalized linear mixed model (GLMM) can be used to analyze collected data. GLMMs are becoming more common in many disciplines and may be preferred over analysis of variance for non-normally distributed data. We evaluated several GLMMs for analyzing the incidence of Phomopsis leaf blight of strawberry in relation to fungicide treatments in five experiments which varied greatly in mean incidence and the differences in incidence between treatments. The first model form accounted for heterogeneity through the residual variance (i.e., the overdispersion parameter), which was assumed to be either fixed for the experiment, or dependent on either treatment or incidence. The second model form accounted for heterogeneity explicitly through a within-plot sampling variance, which was assumed to be either constant or dependent on treatment. All GLMMs could be successfully fitted to the data in each experiment, but there was weak evidence based on the conditional deviance and residual plots that the residual-variance models were more appropriate than the sampling-variance models. Model choice had only a minor effect on F tests for treatment effects and significant differences between treatment means. Based on ease of use and evaluation results, we recommend that the simplest (fixed residual variance) model be used as the first choice in analyzing disease incidence data using GLMMs.
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Salvatore, Fiorella Pia, Alessia Spada, Francesca Fortunato, Demetris Vrontis, and Mariantonietta Fiore. "Identification of Health Expenditures Determinants: A Model to Manage the Economic Burden of Cardiovascular Disease." International Journal of Environmental Research and Public Health 18, no. 9 (April 27, 2021): 4652. http://dx.doi.org/10.3390/ijerph18094652.
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The purpose of this paper is to investigate the determinants influencing the costs of cardiovascular disease in the regional health service in Italy’s Apulia region from 2014 to 2016. Data for patients with acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AF) were collected from the hospital discharge registry. Generalized linear models (GLM), and generalized linear mixed models (GLMM) were used to identify the role of random effects in improving the model performance. The study was based on socio-demographic variables and disease-specific variables (diagnosis-related group, hospitalization type, hospital stay, surgery, and economic burden of the hospital discharge form). Firstly, both models indicated an increase in health costs in 2016, and lower spending values for women (p < 0.001) were shown. GLMM indicates a significant increase in health expenditure with increasing age (p < 0.001). Day-hospital has the lowest cost, surgery increases the cost, and AMI is the most expensive pathology, contrary to AF (p < 0.001). Secondly, AIC and BIC assume the lowest values for the GLMM model, indicating the random effects’ relevance in improving the model performance. This study is the first that considers real data to estimate the economic burden of CVD from the regional health service’s perspective. It appears significant for its ability to provide a large set of estimates of the economic burden of CVD, providing information to managers for health management and planning.
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Groll, Andreas, and Jasmin Abedieh. "Spain retains its title and sets a new record – generalized linear mixed models on European football championships." Journal of Quantitative Analysis in Sports 9, no. 1 (March 30, 2013): 51–66. http://dx.doi.org/10.1515/jqas-2012-0046.
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AbstractNowadays many approaches that analyze and predict the results of football matches are based on bookmakers’ ratings. It is commonly accepted that the models used by the bookmakers contain a lot of expertise as the bookmakers’ profits and losses depend on the performance of their models. One objective of this article is to analyze the role of bookmakers’ odds together with many additional, potentially influental covariates with respect to a national team’s success at European football championships and especially to detect covariates, which are able to explain parts of the information covered by the odds. Therefore a pairwise Poisson model for the number of goals scored by national teams competing in European football championship matches is used. Moreover, the generalized linear mixed model (GLMM) approach, which is a widely used tool for modeling cluster data, allows to incorporate team-specific random effects. Two different approaches to the fitting of GLMMs incorporating variable selection are used, subset selection as well as a Lasso-type technique, including an L1-penalty term that enforces variable selection and shrinkage simultaneously. Based on the two preceeding European football championships a sparse model is obtained that is used to predict all matches of the current tournament resulting in a possible course of the European football championship (EURO) 2012.
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Merritt, Ronald. "Utilizing the Generalized Linear Mixed Model for Specification and Simulation of Transient Vibration Environments." Journal of the IEST 53, no. 2 (October 1, 2010): 35–49. http://dx.doi.org/10.17764/jiet.53.2.y7291022622225x3.
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Transient vibration environments are an important consideration in qualification of aircraft store components — particularly for aircraft with internal storage bays. Generally, these transient vibration environments provide high stimulus input to a store via aerodynamic forces for up to 15 seconds on numerous occasions during training. With the recent introduction of the technique of Time Waveform Replication (TWR) to laboratory testing (MIL-STD-810G Method 525), store components can be readily tested to replications of field-measured transient vibration environments. This paper demonstrates the use of the Generalized Linear Mixed Model (GLMM) on a collection of measured field responses for specification of transient vibration environments. The paper establishes a basis for moving from (1) transient vibration measured field response to (2) transient vibration stochastic specification of the measured field response to (3) laboratory simulation of transient vibration environments.
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Mielenz, Norbert, Joachim Spilke, and Eberhard von Borell. "Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars." Archives Animal Breeding 57, no. 1 (January 29, 2015): 1–19. http://dx.doi.org/10.7482/0003-9438-57-026.
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Abstract. Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.
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Mielenz, Norbert, Joachim Spilke, and Eberhard von Borell. "Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars." Archives Animal Breeding 57, no. 1 (January 29, 2015): 1–19. http://dx.doi.org/10.5194/aab-57-26-2015.
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Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.
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Faris, Richard, and Neil Paton. "121 Statistical Analysis Method Counts for Sow Count Data Responses." Journal of Animal Science 99, Supplement_1 (May 1, 2021): 56. http://dx.doi.org/10.1093/jas/skab054.094.
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Abstract Several statistical analysis methods are typically employed to analyze sow reproductive count data. The research objective was to compare analysis methods of pig birth counts to determine their robustness in identifying simulated treatment differences. Counts of stillborn (SB), born alive (BA) and sow parity differences were simulated using descriptive statistics from a sow farm. Different scenarios were tested: 1) Effect of a 0.5, 1.0, 1.5, and 2.0 percentage point change in treatment difference in SB and BA and, 2) Replicates of 20 to 200 experimental units (EU) in increments of 20 sows; yielding 40 total scenarios. For each scenario, sow observations were simulated 1000 times over. Random sub-setting was used to create a random effect of parity in each dataset as follows: 20% Parity 1, 50% Parity 2–4, and 30% Parity 5+ sows. Each simulated scenario was analyzed as: 1) General linear model (GLM) with raw counts of number of SB or BA as the response variable, 2) GLM with the ratio of BA or SB to total born as the response variable, and 3) Generalized linear mixed model (GLMM) with a binomial distribution of SB or BA as events and total born as trials. Across the EU replicate range, gross performance of models was compared by measuring area under the curve (AUC) with EU as abscissa and the probability of the simulation being P < 0.05 as ordinate. Simulation results are provided in Table 1. The GLMM has elevated probability of detecting true treatment differences over both GLM models for SB and BA. For BA analysis, the GLM Model 1 the probability of detecting true differences is greatly reduced vs. the other two models. This research indicates that deploying GLMM in analyses is a more effective and improved method to detect true differences in sow count data.
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Masuda, Michele M., and Robert P. Stone. "Bayesian logistic mixed-effects modelling of transect data: relating red tree coral presence to habitat characteristics." ICES Journal of Marine Science 72, no. 9 (September 18, 2015): 2674–83. http://dx.doi.org/10.1093/icesjms/fsv163.
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Abstract The collection of continuous data on transects is a common practice in habitat and fishery stock assessments; however, the application of standard regression models that assume independence to serially correlated data is problematic. We show that generalized linear mixed models (GLMMs), i.e. generalized linear models for longitudinal data, that are normally used for studies performed over time can also be applied to other types of clustered or serially correlated data. We apply a specific GLMM for longitudinal data, a hierarchical Bayesian logistic mixed-effects model (BLMM), to a marine ecology dataset obtained from submersible video recordings of the seabed on transects at two sites in the Gulf of Alaska. The BLMM was effective in relating the presence of red tree corals (Primnoa pacifica; i.e. binary data) to habitat characteristics: the presence of red tree corals is highly associated with bedrock as the primary substrate (estimated odds ratio 9–19), high to very high seabed roughness (estimated odds ratio 3–5), and medium to high slope (estimated odds ratio 2–3). The covariate depth was less important at the sites. We also demonstrate and compare two methods of model checking: full and mixed posterior predictive assessments, the latter of which provided a more realistic assessment, and we calculate the variance partition coefficient for reporting the variation explained by multiple levels of the hierarchical model.
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Collins, Gavin, Jennifer P. Lundine, and Eloise Kaizar. "Bayesian Generalized Linear Mixed-Model Analysis of Language Samples: Detecting Patterns in Expository and Narrative Discourse of Adolescents With Traumatic Brain Injury." Journal of Speech, Language, and Hearing Research 64, no. 4 (April 14, 2021): 1256–70. http://dx.doi.org/10.1044/2020_jslhr-20-00471.
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Purpose Generalized linear mixed-model (GLMM) and Bayesian methods together provide a framework capable of handling a wide variety of complex data commonly encountered across the communication sciences. Using language sample analysis, we demonstrate the utility of these methods in answering specific questions regarding the differences between discourse patterns of children who have experienced a traumatic brain injury (TBI), as compared to those with typical development. Method Language samples were collected from 55 adolescents ages 13–18 years, five of whom had experienced a TBI. We describe parameters relating to the productivity, syntactic complexity, and lexical diversity of language samples. A Bayesian GLMM is developed for each parameter of interest, relating these parameters to age, sex, prior history (TBI or typical development), and socioeconomic status, as well as the type of discourse sample (compare–contrast, cause–effect, or narrative). Statistical models are thoroughly described. Results Comparing the discourse of adolescents with TBI to those with typical development, substantial differences are detected in productivity and lexical diversity, while differences in syntactic complexity are more moderate. Female adolescents exhibited greater syntactic complexity, while male adolescents exhibited greater productivity and lexical diversity. Generally, our models suggest more advanced discourse among adolescents who are older or who have indicators of higher socioeconomic status. Differences relating to lecture type were also detected. Conclusions Bayesian and GLMM methods yield more informative and intuitive results than traditional statistical analyses, with a greater degree of confidence in model assumptions. We recommend that these methods be used more widely in language sample analysis. Supplemental Material https://doi.org/10.23641/asha.14226959
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Lazar, Ann A., and Gary O. Zerbe. "Solutions for Determining the Significance Region Using the Johnson-Neyman Type Procedure in Generalized Linear (Mixed) Models." Journal of Educational and Behavioral Statistics 36, no. 6 (December 2011): 699–719. http://dx.doi.org/10.3102/1076998610396889.
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Researchers often compare the relationship between an outcome and covariate for two or more groups by evaluating whether the fitted regression curves differ significantly. When they do, researchers need to determine the “significance region,” or the values of the covariate where the curves significantly differ. In analysis of covariance (ANCOVA), the Johnson-Neyman procedure can be used to determine the significance region; for the hierarchical linear model (HLM), the Miyazaki and Maier (M-M) procedure has been suggested. However, neither procedure can assume nonnormally distributed data. Furthermore, the M-M procedure produces biased (downward) results because it uses the Wald test, does not control the inflated Type I error rate due to multiple testing, and requires implementing multiple software packages to determine the significance region. In this article, we address these limitations by proposing solutions for determining the significance region suitable for generalized linear (mixed) model (GLM or GLMM). These proposed solutions incorporate test statistics that resolve the biased results, control the Type I error rate using Scheffé’s method, and uses a single statistical software package to determine the significance region.
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RUSYANA, ASEP, KHAIRIL ANWAR NOTODIPUTRO, and BAGUS SARTONO. "A generalized linear mixed model for understanding determinant factors of student's interest in pursuing bachelor's degree at Universitas Syiah Kuala." Jurnal Natural 21, no. 2 (June 24, 2021): 72–80. http://dx.doi.org/10.24815/jn.v21i2.19325.
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Generalized Linear Mixed Model (GLMM) is a framework that has a response variable, fixed effects, and random effects. The response variable comes from an exponential family, whereas random effects have a normal distribution. Estimating parameters can be calculated using the maximum likelihood method using the Laplace approach or the Gauss-Hermite Quadrature (GHQ) approach. The purpose of this study was to identify factors that trigger student's interest to continue studying at Universitas Syiah Kuala (USK) using both techniques. The GLMM is suitable for the data because the variable response has a Bernoulli distribution, and the random effects are assumed to be having a normal distribution. Also, the model helps identify the relationship between the dependent variable and the predictors. This study utilizes data from six high schools in Banda Aceh city drawn using a two-stage sampling technique. Stage 1, we randomly chose six out of sixteen public senior high schools in Banda Aceh. Stage 2, we selected students from each school from four different major classes. The GLMM model includes one binary response variable, five numerical fixed-effects, and two random effects. The response variable is the interest of high school students to continue study at USK (yes or no). The five fixed effects in the model including scores of collaboration (C), Action (A), Emotion (E), Purposes (P), and Hope (H). Finally, the random effects are schools (S) and majors (M). In this study, both Laplace and GHQ techniques produce identical results. The predictors that can explain student interest are A, E, and H. These predictors have a positive effect. The random effects of schools and majors are not significantly different from zero. The model with three significant predictors is better than the complete predictor model.
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Moineddin, Rahim, Christopher Meaney, and Eva Grunfeld. "On the analysis of composite measures of quality in medical research." Statistical Methods in Medical Research 26, no. 2 (October 8, 2014): 633–60. http://dx.doi.org/10.1177/0962280214553330.
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Composite endpoints are commonplace in biomedical research. The complex nature of many health conditions and medical interventions demand that composite endpoints be employed. Different approaches exist for the analysis of composite endpoints. A Monte Carlo simulation study was employed to assess the statistical properties of various regression methods for analyzing binary composite endpoints. We also applied these methods to data from the BETTER trial which employed a binary composite endpoint. We demonstrated that type 1 error rates are poor for the Negative Binomial regression model and the logistic generalized linear mixed model (GLMM). Bias was minimal and power was highest in the binomial logistic regression model, the linear regression model, the Poisson (corrected for over-dispersion) regression model and the common effect logistic generalized estimating equation (GEE) model. Convergence was poor in the distinct effect GEE models, the logistic GLMM and some of the zero-one inflated beta regression models. Considering the BETTER trial data, the distinct effect GEE model struggled with convergence and the collapsed composite method estimated an effect, which was greatly attenuated compared to other models. All remaining models suggested an intervention effect of similar magnitude. In our simulation study, the binomial logistic regression model (corrected for possible over/under-dispersion), the linear regression model, the Poisson regression model (corrected for over-dispersion) and the common effect logistic GEE model appeared to be unbiased, with good type 1 error rates, power and convergence properties.
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Sharma, Lav, Irene Oliveira, Fernando Raimundo, Laura Torres, and Guilhermina Marques. "Soil Chemical Properties Barely Perturb the Abundance of Entomopathogenic Fusarium oxysporum: A Case Study Using a Generalized Linear Mixed Model for Microbial Pathogen Occurrence Count Data." Pathogens 7, no. 4 (November 16, 2018): 89. http://dx.doi.org/10.3390/pathogens7040089.
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Fusarium oxysporum exhibits insect pathogenicity—however, generalized concerns of releasing phytopathogens within agroecosystems marred its entomopathogenicity-related investigations. In a previous study, soils were sampled from Douro vineyards and adjacent hedgerows. In this study, 80 of those soils were analyzed for their chemical properties and were subsequently co-related with the abundance of entomopathogenic F. oxysporum, after insect baiting of soils with Galleria mellonella and Tenebrio molitor larvae. The soil chemical properties studied were organic matter content; total organic carbon; total nitrogen; available potassium; available phosphorus; exchangeable cations, such as K+, Na+, Ca2+, and Mg2+; pH; total acidity; degree of base saturation; and effective cation exchange capacity. Entomopathogenic F. oxysporum was found in 48 soils, i.e., 60% ± 5.47%, of the total soil samples. Out of the 1280 insect larvae used, 93, i.e., 7.26% ± 0.72%, were found dead by entomopathogenic F. oxysporum. Stepwise deletion of non-significant variables using a generalized linear model was followed by a generalized linear mixed model (GLMM). A higher C:N (logarithmized) (p < 0.001) and lower exchangeable K+ (logarithmized) (p = 0.008) were found significant for higher fungal abundance. Overall, this study suggests that entomopathogenic F. oxysporum is robust with regard to agricultural changes, and GLMM is a useful statistical tool for count data in ecology.
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Kai, Mikihiko, James T. Thorson, Kevin R. Piner, and Mark N. Maunder. "Spatiotemporal variation in size-structured populations using fishery data: an application to shortfin mako (Isurus oxyrinchus) in the Pacific Ocean." Canadian Journal of Fisheries and Aquatic Sciences 74, no. 11 (November 2017): 1765–80. http://dx.doi.org/10.1139/cjfas-2016-0327.
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We develop a length-disaggregated, spatiotemporal, delta-generalized linear mixed model (GLMM) and apply the method to fishery-dependent catch rates of shortfin mako sharks (Isurus oxyrinchus) in the North Pacific. The spatiotemporal model may provide an improvement over conventional time-series and spatially stratified models by yielding more precise and biologically interpretable estimates of abundance. Including length data may provide additional information to better understand life history and habitat partitioning for marine species. Nominal catch rates were standardized using a GLMM framework with spatiotemporal and length composition data. The best-fitting model showed that most hotspots for “immature” shortfin mako occurred in the coastal waters of Japan, while hotspots for “subadult and adult” occurred in the offshore or coastal waters of Japan. We also found that size-specific catch rates provide an indication that there has been a recent increasing trend in stock abundance since 2008.
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Nikoloulopoulos, Aristidis K. "A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests." Statistical Methods in Medical Research 28, no. 10-11 (September 26, 2018): 3286–300. http://dx.doi.org/10.1177/0962280218796685.
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For a particular disease, there may be two diagnostic tests developed, where each of the tests is subject to several studies. A quadrivariate generalised linear mixed model (GLMM) has been recently proposed to joint meta-analyse and compare two diagnostic tests. We propose a D-vine copula mixed model for joint meta-analysis and comparison of two diagnostic tests. Our general model includes the quadrivariate GLMM as a special case and can also operate on the original scale of sensitivities and specificities. The method allows the direct calculation of sensitivity and specificity for each test, as well as the parameters of the summary receiver operator characteristic (SROC) curve, along with a comparison between the SROCs of each test. Our methodology is demonstrated with an extensive simulation study and illustrated by meta-analysing two examples where two tests for the diagnosis of a particular disease are compared. Our study suggests that there can be an improvement on GLMM in fit to data since our model can also provide tail dependencies and asymmetries.
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Michaelsen, Tore Christian. "Summer temperature and precipitation govern bat diversity at northern latitudes in Norway." Mammalia 80, no. 1 (January 1, 2016): 1–9. http://dx.doi.org/10.1515/mammalia-2014-0077.
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AbstractThis study investigated bat diversity in a temperature and precipitation gradient in fiord and valley landscapes of western Norway about 62° N. Equipment for automatic recording of bat calls was distributed in areas ranging from lowlands to alpine habitats with a mean July temperature range of 8–14°C. A general description of species distribution was given and diversity was analysed using both a generalised linear model (GLM) and a mixed-effects model (GLMM). With regard to the sampling design, the data were analysed on a binary scale, where presence or absence of any species other than the northern bat
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Overall, John E., and Scott Tonidandel. "Analysis of Data from a Controlled Repeated Measurements Design with Baseline-Dependent Dropouts." Methodology 3, no. 2 (January 2007): 58–66. http://dx.doi.org/10.1027/1614-2241.3.2.58.
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Abstract. Differences in mean rates of change are of primary interest in many controlled treatment evaluation studies. Generalized linear mixed model (GLMM) procedures are widely conceived to be the preferred method of analysis for repeated measurement designs when there are missing data due to dropouts, but systematic dependence of the dropout probabilities on antecedent or concurrent factors poses a problem for testing the significance of differences in mean rates of change across time in such designs. Controlling for the dependence of dropout probabilities on baseline values poses a special problem because a theoretically correct GLMM random-effects model does not permit including the same baseline score as both covariate and dependent variable. Monte Carlo methods are used herein to evaluate the actual Type 1 error rates and power resulting from two commonly-illustrated GLMM random-effects model formulations for testing the GROUPS × TIMES linear interaction effect in group-randomized repeated measurements designs. The two GLMM model formulations differ by either including or not including baseline scores as a covariate in the attempt to control for imbalance caused by the baseline-dependent dropouts. Results from those analyses are compared with results from a simpler two-stage analysis in which dropout-weighted slope coefficients fitted separately to the available repeated measurements for each subject serve as the dependent variable for an ordinary ANCOVA test for difference in mean rates of change. The Monte Carlo results confirm modestly superior Type 1 error protection but quite superior power for the simpler two-stage analysis of dropout-weighted slope coefficients as compared with those for either of the more mathematically complex GLMM analyses.
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Payne, Elizabeth H., James W. Hardin, Leonard E. Egede, Viswanathan Ramakrishnan, Anbesaw Selassie, and Mulugeta Gebregziabher. "Approaches for dealing with various sources of overdispersion in modeling count data: Scale adjustment versus modeling." Statistical Methods in Medical Research 26, no. 4 (May 31, 2015): 1802–23. http://dx.doi.org/10.1177/0962280215588569.
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Overdispersion is a common problem in count data. It can occur due to extra population-heterogeneity, omission of key predictors, and outliers. Unless properly handled, this can lead to invalid inference. Our goal is to assess the differential performance of methods for dealing with overdispersion from several sources. We considered six different approaches: unadjusted Poisson regression (Poisson), deviance-scale-adjusted Poisson regression (DS-Poisson), Pearson-scale-adjusted Poisson regression (PS-Poisson), negative-binomial regression (NB), and two generalized linear mixed models (GLMM) with random intercept, log-link and Poisson (Poisson-GLMM) and negative-binomial (NB-GLMM) distributions. To rank order the preference of the models, we used Akaike's information criteria/Bayesian information criteria values, standard error, and 95% confidence-interval coverage of the parameter values. To compare these methods, we used simulated count data with overdispersion of different magnitude from three different sources. Mean of the count response was associated with three predictors. Data from two real-case studies are also analyzed. The simulation results showed that NB and NB-GLMM were preferred for dealing with overdispersion resulting from any of the sources we considered. Poisson and DS-Poisson often produced smaller standard-error estimates than expected, while PS-Poisson conversely produced larger standard-error estimates. Thus, it is good practice to compare several model options to determine the best method of modeling count data.
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Stoffel, Martin A., Shinichi Nakagawa, and Holger Schielzeth. "partR2: partitioning R2 in generalized linear mixed models." PeerJ 9 (May 25, 2021): e11414. http://dx.doi.org/10.7717/peerj.11414.
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The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called ‘inclusive’ R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.
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Ivanova, Anna, Geert Molenberghs, and Geert Verbeke. "Fast and highly efficient pseudo-likelihood methodology for large and complex ordinal data." Statistical Methods in Medical Research 26, no. 6 (October 7, 2015): 2758–79. http://dx.doi.org/10.1177/0962280215608213.
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In longitudinal studies, continuous, binary, categorical, and survival outcomes are often jointly collected, possibly with some observations missing. However, when it comes to modeling responses, the ordinal ones have received less attention in the literature. In a longitudinal or hierarchical context, the univariate proportional odds mixed model (POMM) can be regarded as an instance of the generalized linear mixed model (GLMM). When the response of the joint multivariate model encompass ordinal responses, the complexity further increases. An additional problem of model fitting is the size of the collected data. Pseudo-likelihood based methods for pairwise fitting, for partitioned samples and, as introduced in this paper, pairwise fitting within partitioned samples allow joint modeling of even larger numbers of responses. We show that that pseudo-likelihood methodology allows for highly efficient and fast inferences in high-dimensional large datasets.
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Cabezas-Díaz, Sara, Emilio Virgós, Jorge Lozano, and Julián Mangas. "Spatial distribution models in a frugivorous carnivore, the stone marten (Martes foina): is the fleshy-fruit availability a useful predictor?" Animal Biology 60, no. 4 (2010): 423–36. http://dx.doi.org/10.1163/157075610x523297.
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AbstractFleshy-fruit availability is rarely used as a predictor in stone marten (Martes foina) habitat models, despite its frugivorous carnivore diet. Data on stone marten occurrence, habitat structure and fleshy-fruit species abundance was collected along 2 km long survey routes within 2 × 2 km sample plots (n = 30). Two different spatial scales were considered: 1) the entire survey route; and 2) 200 m segments within each 2 km survey route. Data analyses included Poisson General Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) for the first and second approaches, respectively.Strawberry tree (Arbutus unedo) availability was significantly and positively correlated to stone marten occurrence at both spatial scales, particularly for the large-scale model. At the larger scale, a lower correlation to the traditional habitat structure variables was observed. Tree cover was the most important variable in the small-scale model, but strawberry tree availability was also an important predictor. Stone marten abundance was low in areas of high tree cover and absence of strawberry trees; emphasising the prominent role of strawberry trees per se in the abundance of stone martens. Our results indicated that including fine, field-derived estimates of key food resources for species can increase the utility of habitat models.
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Levy, Joseph F., and Marjorie A. Rosenberg. "A Latent Class Approach to Modeling Trajectories of Health Care Cost in Pediatric Cystic Fibrosis." Medical Decision Making 39, no. 5 (July 2019): 593–604. http://dx.doi.org/10.1177/0272989x19859875.
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Introduction. Estimating costs of medical care attributable to treatments over time is difficult due to costs that cannot be explained solely by observed risk factors. Unobserved risk factors cannot be accounted for using standard econometric techniques, potentially leading to imprecise prediction. The goal of this work is to describe methodology to account for latent variables in the prediction of longitudinal costs. Methods. Latent class growth mixture models (LCGMMs) predict class membership using observed risk factors and class-specific distributions of costs over time. Our motivating example models cost of care for children with cystic fibrosis from birth to age 17. We compare a generalized linear mixed model (GLMM) with LCGMMs. Both models use the same covariates and distribution to predict average costs by combinations of observed risk factors. We adopt a Bayesian estimation approach to both models and compare results using the deviance information criterion (DIC). Results. The 3-class LCGMM model has a lower DIC than the GLMM. The LCGMM latent classes include a low-cost group where costs increase slowly over time, a medium-cost group with initial higher costs than the low-cost group and with more rapidly increasing costs at older ages, and a high-cost group with a U-shaped trajectory. The risk profile-specific mixtures of classes are used to predict costs over time. The LCGMM model shows more delineation of costs by age by risk profile and with less uncertainty than the GLMM model. Conclusions. The LCGMM approach creates flexible prediction models when using longitudinal cost data. The Bayesian estimation approach to LCGMM presented fits well into cost-effectiveness modeling where the estimated trajectories and class membership can be used for prediction.
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Hannon, Emily R., Dana M. Calhoun, Sindhu Chadalawada, and Pieter T. J. Johnson. "Circadian rhythms of trematode parasites: applying mixed models to test underlying patterns." Parasitology 145, no. 6 (November 16, 2017): 783–91. http://dx.doi.org/10.1017/s0031182017001706.
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AbstractCircadian rhythms of parasites and their hosts can influence processes such as transmission, pathology and life cycle evolution. For trematode parasites that depend on free-living infectious stages (i.e. cercariae) to move among host species, the timing of parasite release is hypothesized to increase the likelihood of contacting a host. Yet, a persistent challenge in studying such biorhythms involves selection of appropriate analytical techniques. Here, we extend a generalized linear mixed modelling (GLMM) framework to cosinor analyses, thereby allowing flexibility in the statistical distribution of the response variable, incorporation of multiple covariates and inclusion of hierarchical grouping effects. By applying this approach to 93 snails infected with trematode parasites from freshwater pond ecosystems, we detected non-random rhythms in six of eight species, with variation in both the timing of peak cercariae release (between 5:10 and 21:46 h) and its magnitude (between 13 and 386). The use of GLMM yielded more accurate and precise estimates of the cosinor parameters compared with classical least-squares (LS) based on a simulation-based sensitivity analysis. The sensitivity analysis revealed that the amplitude and rhythm-adjusted mean values from the LS models diverged from the true values at some limits. We highlight the importance of novel analytical approaches for evaluating parasite circadian rhythms and investigating their underlying mechanisms.
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Jung, Yeondae, Dohyeong Kim, and Alex R. Piquero. "Spatiotemporal Association Between Temperature and Assaults: A Generalized Linear Mixed-Model Approach." Crime & Delinquency 66, no. 2 (March 22, 2019): 277–302. http://dx.doi.org/10.1177/0011128719834555.
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We aim to analyze the association between temperature and assault at highly disaggregated spatial units with great temporal resolution to investigate their spatiotemporal dynamics. We applied generalized linear mixed models (GLMMs) to assault and weather data from 2015, aggregated weekly at 424 subdistricts in Seoul, South Korea, controlling for various socioeconomic and environmental variables. Analyses revealed a positive and significant linear association between temperature and assaults and a few small but significant interaction effects that relate to an increase in assaults. A more enhanced understanding of the spatiotemporal relationship between temperature and crime would provide useful implications for targeted crime prevention and resource allocations.
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Serrat, A., P. Pons, R. Puig-Gironès, and C. Stefanescu. "Environmental factors influencing butterfly abundance after a severe wildfire in Mediterranean vegetation." Animal Biodiversity and Conservation 38, no. 2 (July 2015): 205–20. http://dx.doi.org/10.32800/abc.2015.38.0207.
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Despite the attention given to the ecology of butterflies, little is known about their community response to wildfires in the Mediterranean region. Here, we evaluated the butterfly assemblage two years after a severe, 13,000 ha wildfire in Catalonia (NE Spain) in relation to the surrounding unburned habitat. Using visual transect censuses we assessed community parameters such as abundance, diversity, species richness and equitability in burned and unburned areas. Correspondence analysis was used to analyse specific composition and relative abundance of species in the community. The influence of environmental variables on the abundance of some common species was analysed using generalized linear mixed models, taking spatial effects into account. No significant differences were found between areas for any of the community parameters, and no dominance was detected in the burned area. The structure of the vegetation and the geographical distribution of transects influenced the ordination of species and transects on the correspondence analysis plot. Generalized linear mixed models (GLMM) results underscored the role of nectar availability, fire and vegetation structure on the abundance of most species studied.
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Xie, Yuan-tao, Zheng-xiao Li, and Rahul A. Parsa. "Extension and Application of Credibility Models in Predicting Claim Frequency." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/6250686.
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In nonlife actuarial science, credibility models are one of the main methods of experience ratemaking. Bühlmann-Straub credibility model can be expressed as a special case of linear mixed models (LMMs) with the underlying assumption of normality. In this paper, we extend the assumption of Bühlmann-Straub model to include Poisson and negative binomial distributions as they are more appropriate for describing the distribution of a number of claims. By using the framework of generalized linear mixed models (GLMMs), we obtain the generalized credibility premiums that contain as particular cases another credibility premium in the literature. Compared to generalized linear mixed models, our extended credibility models also have an advantage in that the credibility factor falls into the range from 0 to 1. The performance of our models in comparison with an existing model in the literature is also evaluated through numerical studies, which shows that our approach produces premium estimates close to the optima. In addition, our proposed model can also be applied to the most commonly used ratemaking approach, namely, the net, the optimal Bonus-Malus system.
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Aljafary, M., D. M. Gillis, and P. Comeau. "Is catch proportional to nominal effort? Conceptual, fleet dynamic, and statistical considerations in catch standardization." Canadian Journal of Fisheries and Aquatic Sciences 76, no. 12 (December 2019): 2332–42. http://dx.doi.org/10.1139/cjfas-2018-0303.
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Often, catch-per-unit-effort (CPUE) standardizations are used to reflect fish abundance. This implies that catch is directly proportional to effort. We examine this using 78 reported catch and effort series in a meta-analysis, correcting for errors-in-variables in the relationship. Though proportionality in the apparent relationship is the average, there is significant variation among fisheries. We then examine the Scotian Shelf haddock (Melanogrammus aeglefinus) fishery in detail. We used a generalized linear mixed model (GLMM) predicting catch-per-set, accounting for annual and within-year variation in fish, fleet activity, aggregation, and vessel locations and differences. In individual trawls, the catch was less than expected from a proportional relationship with effort. The GLMM revealed both interference and facilitation among vessels as well as autocorrelation among sets. The greatest impact on coefficient estimates was seen by allowing the effort coefficient to vary. Temporal aggregation made the catch–effort relationship appear more proportional. We recommend that fisheries researchers standardize catch explicitly, rather than CPUE, and use disaggregated data to more closely match the underlying relationships in the fisheries being examined.
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Briceño, Felipe, Maite Mascaró, and Carlos Rosas. "GLMM-based modelling of growth in juvenile Octopus maya siblings: does growth depend on initial size?" ICES Journal of Marine Science 67, no. 7 (April 29, 2010): 1509–16. http://dx.doi.org/10.1093/icesjms/fsq038.
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Abstract Briceño, F., Mascaró, M., and Rosas, C. 2010. GLMM-based modelling of growth in juvenile Octopus maya siblings: does growth depend on initial size? – ICES Journal of Marine Science, 67: 1509–1516. In most studies on cephalopod growth, variability in initial size is masked by the assumption of a similar growth “starting point” for all hatchlings and by calculating the growth rate (GR) through modelling the average size of individuals through time. Statistical interpretations based on such models are limited because regression assumptions (e.g. homoscedasticity and independence between subjects) are frequently violated. To avoid these limitations, generalized linear mixed modelling was used to model the early growth of two sets of siblings of the holobenthic octopus Octopus maya under controlled conditions. The aim was to (i) determine the effect of initial weight (IW) on the GR of individuals grouped in three size categories (small, medium, and large), (ii) obtain statistically reliable estimates of parameters in an exponential growth model for juveniles up to 105 d old, and (iii) evaluate the influence of hatching date on weight at hatching. Using restricted maximum likelihood, linear models were fitted between (i) IW and final weight (FW) for octopuses in each size category, and (ii) the natural logarithm of IW as a function of time. The models were validated by visually inspecting the residuals. Individual FW depended on IW, but GR did not differ between juveniles of different sizes. The exponential growth model for individuals of all size categories was, with εi ∼ N(0,σ2 [agei]2δ). Hatching date had no effect on hatching size (F = 1.93; p = 0.11). The GR value is similar to those reported for other holobenthic species, and one of the first estimates of the magnitude (δ = 0.20) and structure of the increase in variance of individual weight through time is provided.
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Díaz-Avalos, Carlos, David L. Peterson, Ernesto Alvarado, Sue A. Ferguson, and Julian E. Besag. "Spacetime modelling of lightning-caused ignitions in the Blue Mountains, Oregon." Canadian Journal of Forest Research 31, no. 9 (September 1, 2001): 1579–93. http://dx.doi.org/10.1139/x01-089.
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Generalized linear mixed models (GLMM) were used to study the effect of vegetation cover, elevation, slope, and precipitation on the probability of ignition in the Blue Mountains, Oregon, and to estimate the probability of ignition occurrence at different locations in space and in time. Data on starting location of lightning-caused ignitions in the Blue Mountains between April 1986 and September 1993 constituted the base for the analysis. The study area was divided into a pixeltime array. For each pixeltime location we associated a value of 1 if at least one ignition occurred and 0 otherwise. Covariate information for each pixel was obtained using a geographic information system. The GLMMs were fitted in a Bayesian framework. Higher ignition probabilities were associated with the following cover types: subalpine herbaceous, alpine tundra, lodgepole pine (Pinus contorta Dougl. ex Loud.), whitebark pine (Pinus albicaulis Engelm.), Engelmann spruce (Picea engelmannii Parry ex Engelm.), subalpine fir (Abies lasiocarpa (Hook.) Nutt.), and grand fir (Abies grandis (Dougl.) Lindl.). Within each vegetation type, higher ignition probabilities occurred at lower elevations. Additionally, ignition probabilities are lower in the northern and southern extremes of the Blue Mountains. The GLMM procedure used here is suitable for analysing ignition occurrence in other forested regions where probabilities of ignition are highly variable because of a spatially complex biophysical environment.
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Trianjaya, Beny, Anang Kurnia, and Agus M. Soleh. "KAJIAN PENGARUH PENAMBAHAN INFORMASI GEROMBOL TERHADAP PREDIKSI AREA NIRCONTOH PADA DATA BINOMIAL." Indonesian Journal of Statistics and Its Applications 4, no. 4 (December 24, 2020): 566–78. http://dx.doi.org/10.29244/ijsa.v4i4.333.
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Employment data is one of the important indicators related to the development progress of a country. Labor conditions in the territory of Indonesia can only be compared between times through the Survei Angkatan Kerja Nasional (Sakernas) data. Data generated from Sakernas and published by BPS is the number of employed and unemployed. The obstacle in estimating the semester unemployment rate at the regency/municipality level is the lack of a number of examples. One of the indirect estimates currently developing is small area estimation (SAE). This study developed the generalized linear mixed model (GLMM) by adding cluster information and examines the development of modifications with several model scenarios. The purpose of this study was to develop a prediction model for basic GLMM on a small area approach by adding cluster information as a fixed effect or random effect. The simulation results show that Model-2, a model that adds a fixed effect k-cluster and also adds a mean from the estimated effect of random areas in the sample area, is the best model with the smallest relative bias (RB) and Relative root mean squares error (RRMSE). This model is better than the basic GLMM model (Model-0) and Model-1 (a model which only adds a mean from the estimated random effect area in the sample area). Model-2 is applied to estimate the proportion of unemployed sub-district level in Southeast Sulawesi Province. Estimating the proportion of unemployed with calibration Model-2 produced an estimated aggregation of the unemployment proportion of Southeast Sulawesi Province at 0.0272. These results are similar to BPS (0.0272). Thus, the results of the estimated proportion of unemployment at the sub-district level with a calibration Model-2 can be said to be feasible to use.
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Chen, Peng, Songhua Hu, Qing Shen, Hangfei Lin, and Chi Xie. "Estimating Traffic Volume for Local Streets with Imbalanced Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 3 (March 2019): 598–610. http://dx.doi.org/10.1177/0361198119833347.
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Annual average daily traffic (AADT) is an important measurement used in traffic engineering. Local streets are major components of a road network. However, automatic traffic recorders (ATRs) used to collect AADT are often limited to arterial roads, and such information is, therefore, often unavailable for local streets. Estimating AADT on local streets becomes a necessity as local street traffic continues to grow and the capacity of arterial roads becomes insufficient. A challenge is that an under-represented sample of local street AADT may result in biased estimation. A synthetic minority oversampling technique (SMOTE) is applied to oversample local streets to correct the imbalanced sampling among different road types. A generalized linear mixed model (GLMM) is employed to estimate AADT incorporating various independent variables, including factors of roadway design, socio-demographics, and land use. The model is examined with an AADT dataset from Seattle, WA. Results show that: (1) SMOTE helps to correct imbalanced sampling proportions and improve model performance significantly; (2) the number of lanes and the number of crosswalks are both positively associated with AADT; (3) road segments located in areas with a higher population density or more mixed land use have a higher AADT; (4) distance to the nearest arterial road is negatively correlated with AADT; and (5) AADT creates spatial spillover effects on neighboring road segments. The combination of SMOTE and GLMM improves the estimation accuracy on AADT, which contributes to better data for transportation planning and traffic monitoring, and to cost saving on data collection.
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Cho, S. C., Y. C. Hong, J. W. Kim, S. Park, M. H. Park, J. Hur, E. J. Park, et al. "Association between urine cotinine levels, continuous performance test variables, and attention deficit hyperactivity disorder and learning disability symptoms in school-aged children." Psychological Medicine 43, no. 1 (May 21, 2012): 209–19. http://dx.doi.org/10.1017/s0033291712001109.
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BackgroundWe examined the cross-sectional relationship between environmental tobacco smoke exposure, continuous performance test (CPT) measures, and attention deficit hyperactivity disorder (ADHD) or learning disability symptoms in school-aged children.MethodIn total, 989 children (526 boys, mean age 9.1 ± 0.7 years), recruited from five South Korean cities participated in this study. We used urine cotinine as a biomarker for environmental tobacco smoke exposure, and obtained the children's scores on a CPT. Parents completed the Korean versions of the ADHD Rating Scale – IV (ADHD-RS) and Learning Disability Evaluation Scale (LDES). Using generalized linear mixed model (GLMM), we assessed the associations between urine cotinine concentrations, neuropsychological variables, and symptoms of ADHD and learning disabilities. Additionally, we conducted structural equation models to explore the effects' pathways.ResultsAfter adjusting for a range of relevant covariates, GLMM showed urinary cotinine levels were significantly and positively associated with CPT scores on omission errors, commission errors, response time, and response time variability, and with parent- and teacher-rated ADHD-RS scores. In addition, urine cotinine levels were negatively associated with LDES scores on spelling and mathematical calculations. The structural equation model revealed that CPT variables mediated the association between urine cotinine levels and parental reports of symptoms of ADHD and learning disabilities.ConclusionsOur data indicate that environmental exposure to tobacco smoke is associated with ADHD and learning disabilities in children, and that impairments in attention and inhibitory control probably mediate the effect.
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Giri, Khageswor, and Harry Gorfine. "Application of a mixed modelling approach to standardize catch-per-unit-effort data for an abalone dive fishery in Western Victoria, Australia." Journal of the Marine Biological Association of the United Kingdom 99, no. 1 (January 17, 2018): 187–95. http://dx.doi.org/10.1017/s002531541700203x.
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Despite the prevalence of catch per unit effort (CPUE) as a key metric in fisheries assessments it can be fraught with inherent problems that often cause its use as an index of abundance to become contentious. This is particularly the case with abalone, a sedentary shellfish targeted by commercial dive fishers around the globe. It is common practice to standardize CPUE to at least partly address issues about how well it reflects the actual abundance of a stock. Differences between standardized and unstandardized trends may lead to controversy between scientists and stakeholders when standardized trends provide a less optimistic picture of stock status. It is within this context that we applied Linear Mixed Model (LMM) and Generalized Linear Mixed Model (GLMM) methods to standardize CPUE for the Western Zone blacklip abalone fishery in Victoria, Australia. This fishery was chosen for our evaluation because it included substantial population losses from a disease shock during the middle of the time series. The effects of diver, reef location, month and their interactions with year were included as random effects in these models and the results compared with nominal geometric means. The two standardization methods provided similar standardized CPUE trends and clearly demonstrated that a large proportion of the variance could be attributed to diver and spatial effects. The GLMM seemed to explain more variability in the data and produced better precision for standardized CPUEs than LMM. The temporal trend in variability attributed to divers and spatial scales reveals the impact of disease as well as any homo/heterogeneity effect. The CPUE trends responded to the impact of disease against a backdrop of declining stock, however when compared with the inter-annual pattern in nominal CPUE, the standardized trends showed that the decline immediately following the onset of disease was less precipitous. In contrast to what appeared to be an increase in the nominal series during the more recent post-disease period, there was only a slight non-significant increase observable in the standardized trends.
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Sondhi, Arun, Alessandro Leidi, and Emily Gilbert. "A Small Area Estimation Method for Investigating the Relationship between Public Perception of Drug Problems with Neighborhood Prognostics: Trends in London between 2012 and 2019." International Journal of Environmental Research and Public Health 18, no. 17 (August 26, 2021): 9016. http://dx.doi.org/10.3390/ijerph18179016.
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The correlation of the public’s perception of drug problems with neighborhood characteristics has rarely been studied. The aim of this study was to investigate factors that correlate with public perceptions in London boroughs using the Mayor’s Office for Policing and Crime (MOPAC) Public Attitude Survey between 2012 and 2019. A subject-specific random effect deploying a Generalized Linear Mixed Model (GLMM) using an Adaptive Gaussian Quadrature method with 10 integration points was applied. To obtain time trends across inner and outer London areas, the GLMM was fitted using a Restricted Marginal Pseudo Likelihood method. The perception of drug problems increased with statistical significance in 17 out of 32 London boroughs between 2012 and 2019. These boroughs were geographically clustered across the north of London. Levels of deprivation, as measured by the English Index of Multiple Deprivation, as well as the percentage of local population who were non-UK-born and recorded vehicle crime rates were shown to be positively associated with the public’s perception of drug problems. Conversely, recorded burglary rate was negatively associated with the public’s perception of drug problems in their area. The public are influenced in their perception of drug problems by neighborhood factors including deprivation and visible manifestations of antisocial behavior.
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Alemany, Ramon, Catalina Bolancé, Roberto Rodrigo, and Raluca Vernic. "Bivariate Mixed Poisson and Normal Generalised Linear Models with Sarmanov Dependence—An Application to Model Claim Frequency and Optimal Transformed Average Severity." Mathematics 9, no. 1 (December 31, 2020): 73. http://dx.doi.org/10.3390/math9010073.
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The aim of this paper is to introduce dependence between the claim frequency and the average severity of a policyholder or of an insurance portfolio using a bivariate Sarmanov distribution, that allows to join variables of different types and with different distributions, thus being a good candidate for modeling the dependence between the two previously mentioned random variables. To model the claim frequency, a generalized linear model based on a mixed Poisson distribution -like for example, the Negative Binomial (NB), usually works. However, finding a distribution for the claim severity is not that easy. In practice, the Lognormal distribution fits well in many cases. Since the natural logarithm of a Lognormal variable is Normal distributed, this relation is generalised using the Box-Cox transformation to model the average claim severity. Therefore, we propose a bivariate Sarmanov model having as marginals a Negative Binomial and a Normal Generalized Linear Models (GLMs), also depending on the parameters of the Box-Cox transformation. We apply this model to the analysis of the frequency-severity bivariate distribution associated to a pay-as-you-drive motor insurance portfolio with explanatory telematic variables.
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Biber, P., S. Seifert, M. K. Zaplata, W. Schaaf, H. Pretzsch, and A. Fischer. "Relationships between substrate, surface characteristics, and vegetation in an initial ecosystem." Biogeosciences Discussions 10, no. 3 (March 8, 2013): 4733–80. http://dx.doi.org/10.5194/bgd-10-4733-2013.
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Abstract. Based on a wide range of empirical data we investigated surface and vegetation dynamics in the artificial initial ecosystem "Chicken Creek" (Lusatia, Germany) in the years 2008–2011. We scrutinized three different hypotheses concerning (1) the relations between initial geomorphological and substrate characteristics with surface structure and terrain properties, (2) the effects of the latter on the occurrence of grouped plant species, and (3) vegetation density effects on terrain surface change. Our data comprise annual vegetation monitoring results, terrestrial laser scans twice a year, annual groundwater levels, and initially measured soil characteristics. Using Generalized Linear Models (GLMM) and Generalized Additive Mixed Models (GAMM) we can mostly confirm our hypotheses, revealing statistically significant relations that partly reflect object or period specific effects but also more general processes which mark the transition from a geo-hydro towards a bio-geo-hydro system, where pure geomorphology or substrate feedbacks are changing into vegetation-substrate feedback processes.
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TSAI, CHIA-HUNG. "Regional Divide and National Identity in Taiwan: Evidences from the 2012 Presidential Election." Issues & Studies 52, no. 02 (June 2016): 1650007. http://dx.doi.org/10.1142/s1013251116500077.
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It is widely believed that identity with Taiwanese or Chinese is the major cleavage in Taiwan. People who hold Taiwanese identity tend to vote for the Democratic Progressive Party (DPP) and those who identify themselves as both Chinese and Taiwanese are likely to vote for the Kuomintang. As the proportion of Taiwanese identifiers increases, the geographical difference seems to persist. Whether national identity is associated with regional line and why they are correlated is a pressing question. This paper uses the 2012 presidential election survey data to explore the extent to which regional divide accounts for national identity. Using generalized linear mixed effect model (GLMM), this research finds minor regional divide in terms of ethnicity concentration and economic structure. However, ethnic background is influential on national identity while retrospective evaluation and democratic value are significant predictors. This mixed result suggests that people in Taiwan have united national identity should geographical difference remain or even decrease, and that we should remain watchful about the influence of democratic value and economic concern.