Loading…
Evaluating predictive errors of a complex environmental model using a general linear model and least square means
A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations i...
Saved in:
Published in: | Ecological modelling 2005-08, Vol.186 (3), p.366-374 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c376t-b26b81382df34e7b3e761421c76dc6bddd42663f7a9286a58e454bc9ac64cf683 |
---|---|
cites | cdi_FETCH-LOGICAL-c376t-b26b81382df34e7b3e761421c76dc6bddd42663f7a9286a58e454bc9ac64cf683 |
container_end_page | 374 |
container_issue | 3 |
container_start_page | 366 |
container_title | Ecological modelling |
container_volume | 186 |
creator | Knightes, Christopher D. Cyterski, Michael |
description | A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP). The response variable for the GLM was defined as R-MCMs predictive error: the difference between observed mercury concentrations and modeled mercury concentrations in each lake. Least square means of the response variable are used as an estimate of the magnitude and significance of bias, i.e., a statistically discernable trend in predictive errors for a given lake type, e.g., acidic, stratified, or oligotrophic. Using our approach, we determined lake types where significant over-prediction and under-prediction of epilimnetic total mercury concentration was occurring, i.e., regions in parameter space where the model demonstrated significant bias was distinguished from regions where no significant bias existed. This technique is most effective for finding regions of parameter space where bias is significant. Drawing conclusions concerning regions that show no significant bias can be misleading. The significant interaction terms in the GLM demonstrated that addressing this problem using univariate statistical techniques would lead to a loss of important information. |
doi_str_mv | 10.1016/j.ecolmodel.2005.01.034 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_19427431</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0304380005000591</els_id><sourcerecordid>19427431</sourcerecordid><originalsourceid>FETCH-LOGICAL-c376t-b26b81382df34e7b3e761421c76dc6bddd42663f7a9286a58e454bc9ac64cf683</originalsourceid><addsrcrecordid>eNqFkE1rGzEQhkVpoW7S31Bd2ttu9GVpfQwhTQKBXJKzmJVmg4xWsqVdk_z7rmvTHnMSjJ53XuYh5AdnLWdcX21bdDmO2WNsBWPrlvGWSfWJrHhnRGOY0J_JikmmGtkx9pV8q3XLGOOiEyuyvz1AnGEK6ZXuCvrgpnBAiqXkUmkeKFCXx13EN4rpEEpOI6YJIv1bSOd6DAJ9xYRlmcaQEMr5E5KnEaFOtO5nKEhHhFQvyZcBYsXv5_eCvPy-fb65bx6f7h5urh8bJ42eml7ovuOyE36QCk0v0WiuBHdGe6d7770SWsvBwEZ0GtYdqrXq3QacVm7Qnbwgv057dyXvZ6yTHUN1GCMkzHO1fKOEUZIvoDmBruRaCw52V8II5d1yZo-K7db-U2yPii3jdlG8JH-eK6A6iEOB5EL9H9cbw41iC3d94nC59xCw2OoCJrfoLugm63P4sOsPrRmYPw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19427431</pqid></control><display><type>article</type><title>Evaluating predictive errors of a complex environmental model using a general linear model and least square means</title><source>ScienceDirect Freedom Collection 2022-2024</source><creator>Knightes, Christopher D. ; Cyterski, Michael</creator><creatorcontrib>Knightes, Christopher D. ; Cyterski, Michael</creatorcontrib><description>A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP). The response variable for the GLM was defined as R-MCMs predictive error: the difference between observed mercury concentrations and modeled mercury concentrations in each lake. Least square means of the response variable are used as an estimate of the magnitude and significance of bias, i.e., a statistically discernable trend in predictive errors for a given lake type, e.g., acidic, stratified, or oligotrophic. Using our approach, we determined lake types where significant over-prediction and under-prediction of epilimnetic total mercury concentration was occurring, i.e., regions in parameter space where the model demonstrated significant bias was distinguished from regions where no significant bias existed. This technique is most effective for finding regions of parameter space where bias is significant. Drawing conclusions concerning regions that show no significant bias can be misleading. The significant interaction terms in the GLM demonstrated that addressing this problem using univariate statistical techniques would lead to a loss of important information.</description><identifier>ISSN: 0304-3800</identifier><identifier>EISSN: 1872-7026</identifier><identifier>DOI: 10.1016/j.ecolmodel.2005.01.034</identifier><identifier>CODEN: ECMODT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Animal, plant and microbial ecology ; Biological and medical sciences ; Environmental ; Errors ; Evaluation ; Freshwater ; Fundamental and applied biological sciences. Psychology ; General aspects. Techniques ; General linear model ; Methods and techniques (sampling, tagging, trapping, modelling...)</subject><ispartof>Ecological modelling, 2005-08, Vol.186 (3), p.366-374</ispartof><rights>2005</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-b26b81382df34e7b3e761421c76dc6bddd42663f7a9286a58e454bc9ac64cf683</citedby><cites>FETCH-LOGICAL-c376t-b26b81382df34e7b3e761421c76dc6bddd42663f7a9286a58e454bc9ac64cf683</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16971740$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Knightes, Christopher D.</creatorcontrib><creatorcontrib>Cyterski, Michael</creatorcontrib><title>Evaluating predictive errors of a complex environmental model using a general linear model and least square means</title><title>Ecological modelling</title><description>A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP). The response variable for the GLM was defined as R-MCMs predictive error: the difference between observed mercury concentrations and modeled mercury concentrations in each lake. Least square means of the response variable are used as an estimate of the magnitude and significance of bias, i.e., a statistically discernable trend in predictive errors for a given lake type, e.g., acidic, stratified, or oligotrophic. Using our approach, we determined lake types where significant over-prediction and under-prediction of epilimnetic total mercury concentration was occurring, i.e., regions in parameter space where the model demonstrated significant bias was distinguished from regions where no significant bias existed. This technique is most effective for finding regions of parameter space where bias is significant. Drawing conclusions concerning regions that show no significant bias can be misleading. The significant interaction terms in the GLM demonstrated that addressing this problem using univariate statistical techniques would lead to a loss of important information.</description><subject>Animal, plant and microbial ecology</subject><subject>Biological and medical sciences</subject><subject>Environmental</subject><subject>Errors</subject><subject>Evaluation</subject><subject>Freshwater</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects. Techniques</subject><subject>General linear model</subject><subject>Methods and techniques (sampling, tagging, trapping, modelling...)</subject><issn>0304-3800</issn><issn>1872-7026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><recordid>eNqFkE1rGzEQhkVpoW7S31Bd2ttu9GVpfQwhTQKBXJKzmJVmg4xWsqVdk_z7rmvTHnMSjJ53XuYh5AdnLWdcX21bdDmO2WNsBWPrlvGWSfWJrHhnRGOY0J_JikmmGtkx9pV8q3XLGOOiEyuyvz1AnGEK6ZXuCvrgpnBAiqXkUmkeKFCXx13EN4rpEEpOI6YJIv1bSOd6DAJ9xYRlmcaQEMr5E5KnEaFOtO5nKEhHhFQvyZcBYsXv5_eCvPy-fb65bx6f7h5urh8bJ42eml7ovuOyE36QCk0v0WiuBHdGe6d7770SWsvBwEZ0GtYdqrXq3QacVm7Qnbwgv057dyXvZ6yTHUN1GCMkzHO1fKOEUZIvoDmBruRaCw52V8II5d1yZo-K7db-U2yPii3jdlG8JH-eK6A6iEOB5EL9H9cbw41iC3d94nC59xCw2OoCJrfoLugm63P4sOsPrRmYPw</recordid><startdate>20050825</startdate><enddate>20050825</enddate><creator>Knightes, Christopher D.</creator><creator>Cyterski, Michael</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>20050825</creationdate><title>Evaluating predictive errors of a complex environmental model using a general linear model and least square means</title><author>Knightes, Christopher D. ; Cyterski, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-b26b81382df34e7b3e761421c76dc6bddd42663f7a9286a58e454bc9ac64cf683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Animal, plant and microbial ecology</topic><topic>Biological and medical sciences</topic><topic>Environmental</topic><topic>Errors</topic><topic>Evaluation</topic><topic>Freshwater</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects. Techniques</topic><topic>General linear model</topic><topic>Methods and techniques (sampling, tagging, trapping, modelling...)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Knightes, Christopher D.</creatorcontrib><creatorcontrib>Cyterski, Michael</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Ecological modelling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Knightes, Christopher D.</au><au>Cyterski, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating predictive errors of a complex environmental model using a general linear model and least square means</atitle><jtitle>Ecological modelling</jtitle><date>2005-08-25</date><risdate>2005</risdate><volume>186</volume><issue>3</issue><spage>366</spage><epage>374</epage><pages>366-374</pages><issn>0304-3800</issn><eissn>1872-7026</eissn><coden>ECMODT</coden><abstract>A general linear model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the regional mercury cycling model (R-MCM) to simulate epilimnetic total mercury concentrations in Vermont and New Hampshire lakes based on data gathered through the EPAs Regional Environmental Monitoring and Assessment Program (REMAP). The response variable for the GLM was defined as R-MCMs predictive error: the difference between observed mercury concentrations and modeled mercury concentrations in each lake. Least square means of the response variable are used as an estimate of the magnitude and significance of bias, i.e., a statistically discernable trend in predictive errors for a given lake type, e.g., acidic, stratified, or oligotrophic. Using our approach, we determined lake types where significant over-prediction and under-prediction of epilimnetic total mercury concentration was occurring, i.e., regions in parameter space where the model demonstrated significant bias was distinguished from regions where no significant bias existed. This technique is most effective for finding regions of parameter space where bias is significant. Drawing conclusions concerning regions that show no significant bias can be misleading. The significant interaction terms in the GLM demonstrated that addressing this problem using univariate statistical techniques would lead to a loss of important information.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ecolmodel.2005.01.034</doi><tpages>9</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0304-3800 |
ispartof | Ecological modelling, 2005-08, Vol.186 (3), p.366-374 |
issn | 0304-3800 1872-7026 |
language | eng |
recordid | cdi_proquest_miscellaneous_19427431 |
source | ScienceDirect Freedom Collection 2022-2024 |
subjects | Animal, plant and microbial ecology Biological and medical sciences Environmental Errors Evaluation Freshwater Fundamental and applied biological sciences. Psychology General aspects. Techniques General linear model Methods and techniques (sampling, tagging, trapping, modelling...) |
title | Evaluating predictive errors of a complex environmental model using a general linear model and least square means |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T13%3A05%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluating%20predictive%20errors%20of%20a%20complex%20environmental%20model%20using%20a%20general%20linear%20model%20and%20least%20square%20means&rft.jtitle=Ecological%20modelling&rft.au=Knightes,%20Christopher%20D.&rft.date=2005-08-25&rft.volume=186&rft.issue=3&rft.spage=366&rft.epage=374&rft.pages=366-374&rft.issn=0304-3800&rft.eissn=1872-7026&rft.coden=ECMODT&rft_id=info:doi/10.1016/j.ecolmodel.2005.01.034&rft_dat=%3Cproquest_cross%3E19427431%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c376t-b26b81382df34e7b3e761421c76dc6bddd42663f7a9286a58e454bc9ac64cf683%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=19427431&rft_id=info:pmid/&rfr_iscdi=true |