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Daniel Fortin - One of the best experts on this subject based on the ideXlab platform.
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a multi state Conditional Logistic Regression model for the analysis of animal movement
The Annals of Applied Statistics, 2017Co-Authors: Aurelien Nicosia, Thierry Duchesne, Louispaul Rivest, Daniel FortinAbstract:A multi-state version of an animal movement analysis method based on Conditional Logistic Regression, called Step Selection Function (SSF), is proposed. In ecology SSF is developed from a comparison between the observed location of an animal and randomly sampled locations at each time step. Interpretation of the parameters in the multi-state model and the impact of different sampling schemes for the random locations are discussed. We prove the relationship between the new model, called HMM-SSF, and a random walk model on the plane. This relationship allows one to use both movement characteristics and local discrete choice behaviors when identifying the model’s hidden states. The new HMM-SSF is used to model the movement behavior of GPS-collared bison in Prince Albert National Park, Canada, where it successfully teases apart areas used to forage and to travel. The analysis thus provides valuable insights into how bison adjust their movement to habitat features, thereby revealing spatial determinants of functional connectivity in heterogeneous landscapes.
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robust inference from Conditional Logistic Regression applied to movement and habitat selection analysis
PLOS ONE, 2017Co-Authors: Mariecaroline Prima, Thierry Duchesne, Daniel FortinAbstract:Conditional Logistic Regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14-450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.
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a multi state Conditional Logistic Regression model for the analysis of animal movement
arXiv: Methodology, 2016Co-Authors: Aurelien Nicosia, Thierry Duchesne, Louispaul Rivest, Daniel FortinAbstract:A multi-state version of an animal movement analysis method based on Conditional Logistic Regression, called Step Selection Function (SSF), is proposed. In ecology SSF is developed from a comparison between the observed location of an animal and randomly sampled locations at each time step. Interpretation of the parameters in the multi-state model and the impact of different sampling schemes for the random locations are discussed. We prove the equivalence between the new model and a random walk model on the plane. This equivalence allows one to use both pure movement and local discrete choice behaviors in identifying the model's hidden states. The new method is used to model the movement behavior of GPS-collared bison in Prince Albert National Park, Canada. The multi-state SSF successfully teases apart areas used to forage and to travel. The analysis thus provides valuable insights into how bison adjust their movement to habitat features, thereby revealing spatial determinants of functional connectivity in heterogeneous landscapes.
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Conditional Logistic Regression with longitudinal follow up and individual level random coefficients a stable and efficient two step estimation method
Journal of Computational and Graphical Statistics, 2011Co-Authors: Radu V Craiu, Daniel Fortin, Thierry Duchesne, Sophie BaillargeonAbstract:The analysis of data generated by animal habitat selection studies, by family studies of genetic diseases, or by longitudinal follow-up of households often involves fitting a mixed Conditional Logistic Regression model to longitudinal data composed of clusters of matched case-control strata. The estimation of model parameters by maximum likelihood is especially difficult when the number of cases per stratum is greater than one. In this case, the denominator of each cluster contribution to the Conditional likelihood involves a complex integral in high dimension, which leads to convergence problems in the numerical maximization. In this article we show how these computational complexities can be bypassed using a global two-step analysis for nonlinear mixed effects models. The first step estimates the cluster-specific parameters and can be achieved with standard statistical methods and software based on maximum likelihood for independent data. The second step uses the EM-algorithm in conjunction with conditi...
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mixed Conditional Logistic Regression for habitat selection studies
Journal of Animal Ecology, 2010Co-Authors: Thierry Duchesne, Daniel Fortin, Nicolas CourbinAbstract:1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unConditional (standard) and Conditional Logistic Regressions. While the advantage of mixed-effects models is recognized for standard Logistic Regression, mixed Conditional Logistic Regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed Conditional Logistic Regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects Regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional Logistic Regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This Regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects Conditional Logistic Regression should become a valuable tool for ecological research.
Alireza Esteghamati - One of the best experts on this subject based on the ideXlab platform.
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inconsistency in albuminuria predictors in type 2 diabetes a comparison between neural network and Conditional Logistic Regression
Translational Research, 2013Co-Authors: Afsaneh Morteza, Manouchehr Nakhjavani, Firouzeh Asgarani, Filipe L F Carvalho, Reza Karimi, Alireza EsteghamatiAbstract:Albuminuria is a sensitive marker to predict future cardiovascular events in patients with type 2 diabetes mellitus. However, current studies only use conventional Regression models to discover predictors of albuminuria. We have used 2 different statistical models to predict albuminuria in type 2 diabetes mellitus: a multilayer perception neural network and a Conditional Logistic Regression. Neural network models were used to predict the level of albuminuria in patients with type 2 diabetes mellitus, which include a matched case-control study for the population. For each case, we randomly selected 1 control matched by age and body mass index (BMI). The input variables were sex, duration of diabetes, systolic and diastolic blood pressure, glomerular filtration rate, high-density lipoprotein, low-density lipoprotein, triglyceride, high-density lipoprotein/triglyceride ratio, cholesterol, fasting blood sugar, and glycated hemoglobin. Age and BMI were included only in the neural network model. This model included 4 hidden layers and 1 bias. Relative error of predictions was 0.38% in the training group, 0.52% in the testing group, and 1.20% in the holdout group. The most robust predictors of albuminuria were high-density lipoprotein (21%), cholesterol (14.4%), and systolic blood pressure (9.7%). Using the Conditional Logistic Regression model, glomerular filtration rate, time of onset to diabetes, and sex were significant indicators in the onset of albuminuria. Using a neural network model, we show that high-density lipoprotein is the most important factor in predicting albuminuria in type 2 diabetes mellitus. Our neural network model complements the current risk factor models to improve the care of patients with diabetes.
Nie Shaofa - One of the best experts on this subject based on the ideXlab platform.
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Conditional Logistic Regression analysis on risk factors of ischemic stroke
Journal of Tropical Medicine, 2007Co-Authors: Nie ShaofaAbstract:Objective To evaluate the risk factors of ischemic stroke using Conditional Logistic Regression,and to provide scientific evidence for prevention and intervention of ischemic stroke.Method A 1∶1 matched case-control study was conducted.309 patients with ischemic stroke were selected from two general hospitals in Shenzhen.Sex,age,and ethnic groups matched subjects were selected as control.The relationship between study factors and ischemic stroke were analyzed by Conditional univariate and multivariate Logistic Regression.Result Hypertension,smoking,family tension and high blood sugar were the major independent risk factors of ischemic stroke.The OR value of hypertension,smoking,family tension and high blood sugar was 3.507,5.420,3.990 and 1.183,respectively.Tea-drinking and exercise were the protective factors for ischemic stoke,and their OR values were 0.250 and 0.100,respectively.Conclusion It is a proper measure to prevent and control ischemic stroke in a community population,and to propose healthy life style including proper exercise,control of high blood pressure and weight.
L U Zuxu - One of the best experts on this subject based on the ideXlab platform.
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non Conditional Logistic Regression analysis on risk factors of type 2 diabetes mellitus
Chinese Journal of Public Health, 2006Co-Authors: L U ZuxuAbstract:Objective To explore the risk factors of type 2 diabetes mellitus(T2DM) in the population aged 40 or over in Shenzhen city and to provide scientific evidence for prevention and intervention of type 2 diabetes mellitus.Methods A case-control study was conducted.The relationship between study factors and T2DM were analyzed by unConditional univariate and multivariate Logistic Regression.Results Family history of diabetes mellitus(DM),hypertension,hyperlipidemia and work pressure were the major risk factors and culture level,exercise were the protect factors for T2DM.Conclusion It is an important measure to prevent T2DM in community population to propose healthy life style including proper exercise,control of high blood pressure,high blood fat and weight.
Thierry Duchesne - One of the best experts on this subject based on the ideXlab platform.
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a multi state Conditional Logistic Regression model for the analysis of animal movement
The Annals of Applied Statistics, 2017Co-Authors: Aurelien Nicosia, Thierry Duchesne, Louispaul Rivest, Daniel FortinAbstract:A multi-state version of an animal movement analysis method based on Conditional Logistic Regression, called Step Selection Function (SSF), is proposed. In ecology SSF is developed from a comparison between the observed location of an animal and randomly sampled locations at each time step. Interpretation of the parameters in the multi-state model and the impact of different sampling schemes for the random locations are discussed. We prove the relationship between the new model, called HMM-SSF, and a random walk model on the plane. This relationship allows one to use both movement characteristics and local discrete choice behaviors when identifying the model’s hidden states. The new HMM-SSF is used to model the movement behavior of GPS-collared bison in Prince Albert National Park, Canada, where it successfully teases apart areas used to forage and to travel. The analysis thus provides valuable insights into how bison adjust their movement to habitat features, thereby revealing spatial determinants of functional connectivity in heterogeneous landscapes.
-
robust inference from Conditional Logistic Regression applied to movement and habitat selection analysis
PLOS ONE, 2017Co-Authors: Mariecaroline Prima, Thierry Duchesne, Daniel FortinAbstract:Conditional Logistic Regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14-450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.
-
a multi state Conditional Logistic Regression model for the analysis of animal movement
arXiv: Methodology, 2016Co-Authors: Aurelien Nicosia, Thierry Duchesne, Louispaul Rivest, Daniel FortinAbstract:A multi-state version of an animal movement analysis method based on Conditional Logistic Regression, called Step Selection Function (SSF), is proposed. In ecology SSF is developed from a comparison between the observed location of an animal and randomly sampled locations at each time step. Interpretation of the parameters in the multi-state model and the impact of different sampling schemes for the random locations are discussed. We prove the equivalence between the new model and a random walk model on the plane. This equivalence allows one to use both pure movement and local discrete choice behaviors in identifying the model's hidden states. The new method is used to model the movement behavior of GPS-collared bison in Prince Albert National Park, Canada. The multi-state SSF successfully teases apart areas used to forage and to travel. The analysis thus provides valuable insights into how bison adjust their movement to habitat features, thereby revealing spatial determinants of functional connectivity in heterogeneous landscapes.
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Conditional Logistic Regression with longitudinal follow up and individual level random coefficients a stable and efficient two step estimation method
Journal of Computational and Graphical Statistics, 2011Co-Authors: Radu V Craiu, Daniel Fortin, Thierry Duchesne, Sophie BaillargeonAbstract:The analysis of data generated by animal habitat selection studies, by family studies of genetic diseases, or by longitudinal follow-up of households often involves fitting a mixed Conditional Logistic Regression model to longitudinal data composed of clusters of matched case-control strata. The estimation of model parameters by maximum likelihood is especially difficult when the number of cases per stratum is greater than one. In this case, the denominator of each cluster contribution to the Conditional likelihood involves a complex integral in high dimension, which leads to convergence problems in the numerical maximization. In this article we show how these computational complexities can be bypassed using a global two-step analysis for nonlinear mixed effects models. The first step estimates the cluster-specific parameters and can be achieved with standard statistical methods and software based on maximum likelihood for independent data. The second step uses the EM-algorithm in conjunction with conditi...
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mixed Conditional Logistic Regression for habitat selection studies
Journal of Animal Ecology, 2010Co-Authors: Thierry Duchesne, Daniel Fortin, Nicolas CourbinAbstract:1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unConditional (standard) and Conditional Logistic Regressions. While the advantage of mixed-effects models is recognized for standard Logistic Regression, mixed Conditional Logistic Regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed Conditional Logistic Regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects Regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional Logistic Regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This Regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects Conditional Logistic Regression should become a valuable tool for ecological research.