[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-289942-105":59,"doc-detail-289942-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","other-topics-in-ordination-co-correspondence-analysis","Other Topics in Ordination - Co-correspondence Analysis","","This presentation document covers advanced ordination techniques, focusing specifically on Co-correspondence analysis (Co-CA). It provides an overview of symmetric and predictive Co-CA methods, explaining how they relate two species datasets by maximizing covariation or predicting response data. The material includes practical R code examples using the 'cocorresp' library, demonstrating data transformation and the execution of symmetric Co-CA. It also covers diagnostic tools like screeplots and biplots to visualize and interpret ecological data relationships.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/other-topics-in-ordination-co-correspondence-analysis/289942/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/other-topics-in-ordination-co-correspondence-analysis/289942.png","ImageObject",300,407,{"name":92,"@type":93},"Cart","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the primary purpose of Co-correspondence Analysis (Co-CA)?","Question",{"text":112,"@type":113},"Co-CA is used to relate two species datasets to one another, either symmetrically to maximize their covariation or predictively to use one dataset to predict the other.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What are the two types of Co-CA described?",{"text":117,"@type":113},"The two types are symmetric Co-CA, which finds axes that maximize covariation between two datasets, and predictive Co-CA, which finds directions in one dataset that best predict the response in the other.",{"name":119,"@type":110,"acceptedAnswer":120},"Which R package is used for conducting Co-CA in the provided examples?",{"text":121,"@type":113},"The 'cocorresp' package is used for performing Co-correspondence analysis, as demonstrated by the library(cocorresp) command.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},289942,1790461415,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},18829141979164,"https://eur-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Other topics  \nGavin L. Simpson May 30, 2024  \nWelcome  \n2 / 44  \nOther topics  \n Co-correspondence analysis ( Co-CA)  \n Principal Response Curves ( PRC)  \n Mantel test  \n Ordination diagnostics  \n3 / 44  \nCo-CA  \n4 / 44  \nCo-correspondence analysis  \nWe may be interested in relating two species data sets to one another  \nCan can't do this using CCA with one data set as the response and the other as the predictors, because this would be too many predictors  \nWe might also not want to treat one data set as the response, we might want to analyse the data symmetrically  \nCo-correspondence Analysis ( Co-CA) is a suitable method  \n5 / 44  \nCo-correspondence analysis  \nThere are two types of Co-CA  \n1. symmetric Co-CA, and  \n2. predictive Co-CA  \nSymmetric Co-CA finds axes in both data sets which maximise the covariation of the two data sets  \nPredictive Co-CA finds directions in one data set which best predict the response data set  \n6 / 44  \nSymmetric Co-CA  \nlibrary( \" cocorresp \" )  \ndata(beetles)  \n\\#\\# log transform the beetle data beetles \u003C-log1p(beetles)  \ndata(plants)  \n7 / 44  \nSymmetric Co-CA  \nbp.sym \u003C- coca(beetles ~ . , data = plants , method = \" symmetric \" )  \n\\#\\#  \n\\#\\# Removed some species that contained no data in : beetles , plants  \nbp.sym  \n\\#\\#  \n\\#\\# Symmetric Co-Correspondence Analysis \\#\\#  \n\\#\\# Call : symcoca(y = y , x = x , n.axes = n.axes , R0 = weights , symmetric =\\#\\# symmetric , nam.dat = nam.dat)  \n\\#\\#  \n\\#\\# Inertia :  \n\\#\\# Total Explained Residual  \n\\#\\# beetles : 3 .988 3 .971 0 .018  \n\\#\\# plants : 5 .757 5 .740 0 .018  \n8 / 44  \nSymmetric Co-CA  \nscreeplot(bp.sym)  \n|  | |\n| --- | --- |\n\n9 / 44  \nSymmetric Co-CA  \nlayout(matrix(1 :2 , ncol = 2))  \nbiplot(bp.sym , which = \" y1 \" , main = \" Beetles \" )  \nbiplot(bp.sym , which = \" y2 \" , main = \" Plants \" ) layout(1)  \n10 / 44","cbCaihepFOaYOm1J","https://ap.wps.com/l/cbCaihepFOaYOm1J","pdf",780925,44,"English","# Other Topics\n## Co-correspondence analysis (Co-CA)\n## Principal Response Curves (PRC)\n## Mantel test\n## Ordination diagnostics\n# Co-CA Methods\n## Symmetric Co-CA\n## Predictive Co-CA","[{\"question\":\"What is the primary purpose of Co-correspondence Analysis (Co-CA)?\",\"answer\":\"Co-CA is used to relate two species datasets to one another, either symmetrically to maximize their covariation or predictively to use one dataset to predict the other.\"},{\"question\":\"What are the two types of Co-CA described?\",\"answer\":\"The two types are symmetric Co-CA, which finds axes that maximize covariation between two datasets, and predictive Co-CA, which finds directions in one dataset that best predict the response in the other.\"},{\"question\":\"Which R package is used for conducting Co-CA in the provided examples?\",\"answer\":\"The 'cocorresp' package is used for performing Co-correspondence analysis, as demonstrated by the library(cocorresp) command.\"}]","Other Topics in Ordination - Co-correspondence Analysis | PDF",1789638672,111]