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I'm going to conduct Canonical Correspondence Analysis (CCA). In the tutorial I've found at: CCA environmental data are discrete variables with multiple levels within each variable (please check env.csv file in the tutorial). But in my case some environmental variables belong to nominal and some to ordinal data types with only two levels for each variable. Can I do CCA on my data?Which data types are suitable for CCA? What should I do if my environmental variables would belong to different data types (e.g. continuous, discrete, nominal, etc. with different levels within each variable)?

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    $\begingroup$ Some similar posts. $\endgroup$ – kjetil b halvorsen♦ Aug 7 '19 at 11:50
  • $\begingroup$ Thanks for the link you provided. If i understand correctly the posts are related to Canonical Correlation Analysis instead of Canonical Correspondence Analysis. $\endgroup$ – Denis Aug 7 '19 at 12:13
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    $\begingroup$ Maybe you should look into Gifi there is an R package homals. $\endgroup$ – kjetil b halvorsen♦ Aug 7 '19 at 12:34

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