The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
David I. Warton - One of the best experts on this subject based on the ideXlab platform.
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Fast model-based Ordination with copulas
2021Co-Authors: Gordana C. Popovic, Francis K. C. Hui, David I. WartonAbstract:ABSTRACTVisualising data is a vital part of analysis, allowing researchers to find patterns, and assess and communicate the results of statistical modeling. In ecology, visualisation is often challenging when there are many variables (often for different species or other taxonomic groups) and they are not normally distributed (often counts or presence-absence data). Ordination is a common and powerful way to overcome this hurdle by reducing data from many response variables to just two or three, to be easily plotted.Ordination is traditionally done using dissimilarity-based methods, most commonly non-metric multidimensional scaling (nMDS). In the last decade however, model-based methods for unconstrained Ordination have gained popularity. These are primarily based on latent variable models, with latent variables estimating the underlying, unobserved ecological gradients.Despite some major benefits, a major drawback of model-based Ordination methods is their speed, as they typically taking much longer to return a result than dissimilarity-based methods, especially for large sample sizes.We introduce copula Ordination, a new, scalable model-based approach to unconstrained Ordination. This method has all the desirable properties of model-based Ordination methods, with the added advantage that it is computationally far more efficient. In particular, simulations show copula Ordination is an order of magnitude faster than current model-based methods, and can even be faster than nMDS for large sample sizes, while being able to produce similar Ordination plots and trends as these methods.
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model based approaches to unconstrained Ordination
Methods in Ecology and Evolution, 2015Co-Authors: Francis K. C. Hui, Sara Taskinen, Shirley Pledger, Scott D. Foster, David I. WartonAbstract:Summary Unconstrained Ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained Ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained Ordination can address this issue, and in this study, two types of models for Ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation. Advantages of model-based approaches to Ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for Ordination; methods for formal statistical inference to draw conclusions from the Ordination; and improved efficiency, that is model-based Ordination better recovers true relationships between sites, when used appropriately.
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Model‐based approaches to unconstrained Ordination
Methods in Ecology and Evolution, 2014Co-Authors: Francis K. C. Hui, Sara Taskinen, Shirley Pledger, Scott D. Foster, David I. WartonAbstract:Summary Unconstrained Ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained Ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained Ordination can address this issue, and in this study, two types of models for Ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation. Advantages of model-based approaches to Ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for Ordination; methods for formal statistical inference to draw conclusions from the Ordination; and improved efficiency, that is model-based Ordination better recovers true relationships between sites, when used appropriately.
Francis K. C. Hui - One of the best experts on this subject based on the ideXlab platform.
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Fast model-based Ordination with copulas
2021Co-Authors: Gordana C. Popovic, Francis K. C. Hui, David I. WartonAbstract:ABSTRACTVisualising data is a vital part of analysis, allowing researchers to find patterns, and assess and communicate the results of statistical modeling. In ecology, visualisation is often challenging when there are many variables (often for different species or other taxonomic groups) and they are not normally distributed (often counts or presence-absence data). Ordination is a common and powerful way to overcome this hurdle by reducing data from many response variables to just two or three, to be easily plotted.Ordination is traditionally done using dissimilarity-based methods, most commonly non-metric multidimensional scaling (nMDS). In the last decade however, model-based methods for unconstrained Ordination have gained popularity. These are primarily based on latent variable models, with latent variables estimating the underlying, unobserved ecological gradients.Despite some major benefits, a major drawback of model-based Ordination methods is their speed, as they typically taking much longer to return a result than dissimilarity-based methods, especially for large sample sizes.We introduce copula Ordination, a new, scalable model-based approach to unconstrained Ordination. This method has all the desirable properties of model-based Ordination methods, with the added advantage that it is computationally far more efficient. In particular, simulations show copula Ordination is an order of magnitude faster than current model-based methods, and can even be faster than nMDS for large sample sizes, while being able to produce similar Ordination plots and trends as these methods.
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model based approaches to unconstrained Ordination
Methods in Ecology and Evolution, 2015Co-Authors: Francis K. C. Hui, Sara Taskinen, Shirley Pledger, Scott D. Foster, David I. WartonAbstract:Summary Unconstrained Ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained Ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained Ordination can address this issue, and in this study, two types of models for Ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation. Advantages of model-based approaches to Ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for Ordination; methods for formal statistical inference to draw conclusions from the Ordination; and improved efficiency, that is model-based Ordination better recovers true relationships between sites, when used appropriately.
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Model‐based approaches to unconstrained Ordination
Methods in Ecology and Evolution, 2014Co-Authors: Francis K. C. Hui, Sara Taskinen, Shirley Pledger, Scott D. Foster, David I. WartonAbstract:Summary Unconstrained Ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained Ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained Ordination can address this issue, and in this study, two types of models for Ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation. Advantages of model-based approaches to Ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for Ordination; methods for formal statistical inference to draw conclusions from the Ordination; and improved efficiency, that is model-based Ordination better recovers true relationships between sites, when used appropriately.
P Kannan - One of the best experts on this subject based on the ideXlab platform.
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co Ordination polymers
Progress in Polymer Science, 2000Co-Authors: T Kaliyappan, P KannanAbstract:Abstract This paper is a review of the metal complex forming coOrdination polymers. A polymer–metal complex is composed of synthetic polymer and metal ions bound to the polymer ligand by a coordinate bond. A polymer ligand contains anchoring sites like nitrogen, oxygen or sulphur obtained either by the polymerization of monomer possessing the coordinating site or by a chemical reaction between a polymer and a low molecular weight compound having the coordinating ability. The polymer–metal complexes may be classified into different groups according to the position occupied by the metal, which is decided by the method of preparation. The method include the complexation of polymeric ligand with various metal ions, cross-linked polymers with pendent, ligands forming either intramolecular and/or intermolecular chelating functions are highlighted in the first part. The various works on the coOrdination complexes has revealed that the heterogeneous systems possess more economical potentials and advantages over homogeneous systems. The co-Ordination polymers belong to the former case. The high molecular weight polymer–metal complexes work as storage houses for solar energy. Efficient chemical conversion in the storage of solar energy will be difficult with the homogeneous systems. The synthesis results in an organic polymer with inorganic functions. The metal atoms attached to the polymer backbone are bound to exhibit characteristic catalytic behaviour, which are distinctly different from their low molecular weight analogue. Many synthetic polymer–metal complexes, found to possess high catalytic efficiency, in addition to ion selectivity in waste water treatment, recovery of trace metal ions, and hydrometallurgy are enlightened in the final part.
Jim Drake - One of the best experts on this subject based on the ideXlab platform.
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Vegetation classification, mapping, and monitoring at Voyageurs National Park, Minnesota: An application of the U.S. National Vegetation Classification
Applied Vegetation Science, 2007Co-Authors: Don Faber-langendoen, Norm Aaseng, Michael Smith, Jim DrakeAbstract:ABSTRACT Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with ‘alliance’ and ‘association’ as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type Ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associati...
Scott D. Foster - One of the best experts on this subject based on the ideXlab platform.
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model based approaches to unconstrained Ordination
Methods in Ecology and Evolution, 2015Co-Authors: Francis K. C. Hui, Sara Taskinen, Shirley Pledger, Scott D. Foster, David I. WartonAbstract:Summary Unconstrained Ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained Ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained Ordination can address this issue, and in this study, two types of models for Ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation. Advantages of model-based approaches to Ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for Ordination; methods for formal statistical inference to draw conclusions from the Ordination; and improved efficiency, that is model-based Ordination better recovers true relationships between sites, when used appropriately.
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Model‐based approaches to unconstrained Ordination
Methods in Ecology and Evolution, 2014Co-Authors: Francis K. C. Hui, Sara Taskinen, Shirley Pledger, Scott D. Foster, David I. WartonAbstract:Summary Unconstrained Ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance. Methods of unconstrained Ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results. A model-based approach to unconstrained Ordination can address this issue, and in this study, two types of models for Ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation. Advantages of model-based approaches to Ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for Ordination; methods for formal statistical inference to draw conclusions from the Ordination; and improved efficiency, that is model-based Ordination better recovers true relationships between sites, when used appropriately.
