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{"title"=>"Bayesian multimodel inference for geostatistical regression models", "type"=>"journal", "authors"=>[{"first_name"=>"Devin S.", "last_name"=>"Johnson", "scopus_author_id"=>"7406828372"}, {"first_name"=>"Jennifer A.", "last_name"=>"Hoeting", "scopus_author_id"=>"6602650470"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "sgr"=>"80755125967", "scopus"=>"2-s2.0-80755125967", "isbn"=>"1932-6203", "pui"=>"362897857", "doi"=>"10.1371/journal.pone.0025677", "pmid"=>"22102854"}, "id"=>"fc3352d9-3fa8-34be-9ef3-13de5dacf57f", "abstract"=>"The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance.", "link"=>"http://www.mendeley.com/research/bayesian-multimodel-inference-geostatistical-regression-models", "reader_count"=>39, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Researcher"=>9, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Researcher"=>9, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>3, "Environmental Science"=>8, "Nursing and Health Professions"=>1, "Mathematics"=>3, "Agricultural and Biological Sciences"=>10, "Medicine and Dentistry"=>2, "Veterinary Science and Veterinary Medicine"=>1, "Psychology"=>1, "Social Sciences"=>4, "Computer Science"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>4}, "Psychology"=>{"Psychology"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>8}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Australia"=>1, "Switzerland"=>1, "Portugal"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>1}

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• http://doi.org/10.1111/geb.12256
• http://doi.org/10.1890/14-0661.1
• http://doi.org/10.1002/env.2440
• http://doi.org/10.1007/s13253-013-0143-0
• http://doi.org/10.1016/j.regsciurbeco.2013.02.002
• http://doi.org/10.1111/ecog.01633
• http://doi.org/10.1007/s10980-017-0575-y

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/713495"], "description"=>"<p>Included are coefficients are: Ant (), log % sand (), Elevation (), and % cover (). Vertical bars represent and the density curve is a kernel estimate conditioned on .</p>", "links"=>[], "tags"=>["posterior", "estimates", "pip", "regression", "coefficients", "lizard", "abundance"], "article_id"=>383853, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Marginal_posterior_density_estimates_for_the_top_4_PIP_regression_coefficients_in_the_lizard_abundance_analysis_/383853", "title"=>"Marginal posterior density estimates for the top 4 PIP regression coefficients in the lizard abundance analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-11-10 01:04:13"}
• {"files"=>["https://ndownloader.figshare.com/files/713556"], "description"=>"<p>Included are coefficients are: Strahler order (), Watershed area (), % watershed area disturbed by human use (), and habitat quality (). Vertical bars represent and the density curve is a kernel estimate conditioned on .</p>", "links"=>[], "tags"=>["posterior", "estimates", "pip", "regression", "coefficients", "abundance"], "article_id"=>383916, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Marginal_posterior_density_estimates_for_the_top_four_PIP_regression_coefficients_in_the_fish_abundance_analysis_/383916", "title"=>"Marginal posterior density estimates for the top four PIP regression coefficients in the fish abundance analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-11-10 01:05:16"}
• {"files"=>["https://ndownloader.figshare.com/files/713630"], "description"=>"<p>Listed are the explanatory covariates selected using the PMP criterion. The table is ordered according to the PMPs of the informative prior analysis.</p>", "links"=>[], "tags"=>["marine and aquatic sciences", "computer science", "mathematics"], "article_id"=>383994, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.t005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_selection_results_for_the_fish_tolerance_data_set_/383994", "title"=>"Model selection results for the fish tolerance data set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-11-10 01:06:34"}
• {"files"=>["https://ndownloader.figshare.com/files/713680"], "description"=>"<p>The top five models in PMP are shown. The PARJ chain visited 114 out of 128 possible models in the spatial analysis and 90 models were visited in the independence model. The table is ordered by PMP of the spatial regression analysis.</p>★<p>indicates PMP1.0 and Rank18.</p>", "links"=>[], "tags"=>["california", "lizard"], "article_id"=>384045, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.t003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_selection_results_for_the_California_lizard_data_set_/384045", "title"=>"Model selection results for the California lizard data set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-11-10 01:07:25"}
• {"files"=>["https://ndownloader.figshare.com/files/361708", "https://ndownloader.figshare.com/files/361774", "https://ndownloader.figshare.com/files/361917"], "description"=>"<div><p>The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows <em>a priori</em> unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance.</p> </div>", "links"=>[], "tags"=>["bayesian", "multimodel", "inference", "geostatistical", "regression", "models"], "article_id"=>131504, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0025677.s001", "https://dx.doi.org/10.1371/journal.pone.0025677.s002", "https://dx.doi.org/10.1371/journal.pone.0025677.s003"], "stats"=>{"downloads"=>6, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Bayesian_Multimodel_Inference_for_Geostatistical_Regression_Models/131504", "title"=>"Bayesian Multimodel Inference for Geostatistical Regression Models", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-11-10 00:25:04"}
• {"files"=>["https://ndownloader.figshare.com/files/713657"], "description"=>"<p>The first column of probabilities are results from a spatial analysis with nugget and anisotropy parameters present in the model. The second column of probabilities resulted from an independence model.</p>", "links"=>[], "tags"=>["inclusion", "probabilities", "california", "lizard"], "article_id"=>384014, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.t004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Posterior_inclusion_probabilities_PIP_for_the_California_lizard_data_/384014", "title"=>"Posterior inclusion probabilities (PIP) for the California lizard data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-11-10 01:06:54"}
• {"files"=>["https://ndownloader.figshare.com/files/713712"], "description"=>"<p>Each proposal was a random walk, therefore it is centered on the previous parameter value in the RJMCMC iteration.</p>", "links"=>[], "tags"=>["distributions", "rjmcmc", "moves"], "article_id"=>384076, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proposal_distributions_for_RJMCMC_within_model_moves_of_each_analysis_/384076", "title"=>"Proposal distributions for RJMCMC within model moves of each analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-11-10 01:07:56"}
• {"files"=>["https://ndownloader.figshare.com/files/713737"], "description"=>"<p>The pollution intolerant fish abundance data were analyzed using the following model and parameter updating scheme. All Metropolis proposal distributions are random walks centered on the current parameter value.</p>a<p>Metropolis proposal distributions are centered on the current parameter value.</p>b<p>Full conditional distribution.</p>c<p>Step 7 is only necessary for spatial GLMMs.</p>", "links"=>[], "tags"=>["parj", "algorithm", "geostatistical", "regression"], "article_id"=>384095, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Steps_in_PARJ_algorithm_for_geostatistical_regression_models_/384095", "title"=>"Steps in PARJ algorithm for geostatistical regression models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-11-10 01:08:15"}
• {"files"=>["https://ndownloader.figshare.com/files/713607"], "description"=>"<p>The first column gives PIPs for the informative model prior analysis. The second column gives PIPs for the flat model prior analysis.</p>", "links"=>[], "tags"=>["inclusion", "probabilities", "maha", "intolerant"], "article_id"=>383967, "categories"=>["Information And Computing Sciences", "Mathematics", "Inorganic Chemistry"], "users"=>["Devin S. Johnson", "Jennifer A. Hoeting"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025677.t006", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Posterior_inclusion_probabilities_PIP_for_the_MAHA_pollution_intolerant_fish_data_/383967", "title"=>"Posterior inclusion probabilities (PIP) for the MAHA pollution intolerant fish data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-11-10 01:06:07"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime