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r_workshop9 [2019/03/18 04:12] mariehbrice |
r_workshop9 [2021/04/23 13:57] lsherin |
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| //The content of this workshop has been peer-reviewed by several QCBS members. If you would like to suggest modifications, please contact the current series coordinators, listed on the main wiki page// | //The content of this workshop has been peer-reviewed by several QCBS members. If you would like to suggest modifications, please contact the current series coordinators, listed on the main wiki page// | ||
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| + | <wrap em>IMPORTANT NOTICE: MAJOR UPDATES</wrap> | ||
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| + | **March 2021 update:** This wiki has been discontinued and is no longer being actively developed or updated. Updated materials for the QCBS R Workshop Series are now housed on the QCBS R Workshop [[https://github.com/QCBSRworkshops/workshop09|GitHub page]]. | ||
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| + | Available materials include; | ||
| + | - The [[https://qcbsrworkshops.github.io/workshop09/pres-en/workshop09-pres-en.html|Rmarkdown presentation]] for this workshop; | ||
| + | - An [[https://qcbsrworkshops.github.io/workshop09/book-en/workshop09-script-en.R|R script]] that follows the presentation; | ||
| + | - [[https://qcbsrworkshops.github.io/workshop09/book-en/index.html|Written materials]] that accompany the presentation in bookdown format. | ||
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| (Material in R script obtained from: Borcard, Gillet & Legendre (2011). //Numerical Ecology with R//. Springer New York.) | (Material in R script obtained from: Borcard, Gillet & Legendre (2011). //Numerical Ecology with R//. Springer New York.) | ||
| - | **Summary:** In this workshop, you will learn the basics of multivariate analyses for your own data. You will learn to choose appropriate distance metrics and transformations for clustering, unconstrained ordinations, and to perform Principal Component Analysis, Correspondence Analysis, Principal Coordinate Analysis and Nonmetric MultiDimensional Scaling, to find patterns in your community composition data! | + | **Summary:** In this workshop, you will learn the basics of multivariate analyses that will allow you to reveal patterns in your community composition data. You will first learn to choose appropriate distance metrics and transformations to then perform various multivariate analyses: clustering analysis, Principal Component Analysis (PCA), Correspondence Analysis (CA), Principal Coordinate Analysis (PCoA) and Non-Metric MultiDimensional Scaling (NMDS). |
| - | **Link to new [[https://qcbsrworkshops.github.io/Workshops/workshop09/workshop09-en/workshop09-en.html|Rmarkdown presentation]]** | + | **Link to new [[https://qcbsrworkshops.github.io/workshop09/workshop09-en/workshop09-en.html|Rmarkdown presentation]]** |
| Link to old [[https://prezi.com/isb-5h_tanxo/|Prezi presentation]] | Link to old [[https://prezi.com/isb-5h_tanxo/|Prezi presentation]] | ||
