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r_workshop9 [2019/03/18 04:12]
mariehbrice
r_workshop9 [2021/04/23 13:57] (current)
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]]. ​
 +
 +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]]