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原产地美国
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适用平台Windows
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In the world of increasing use of open-sourced statistical platforms like R, some might wonder if there is a need for proprietary stats programs. Despite this trend, I find myself still going back to PC-ORD, even though I also use R, Python and other scripting platforms. The reason I keep using PC-ORD is simple: PC-ORD is clearly superior to trying to code the same functionality in R or other popular open-source analytical platforms. Another reason I keep using PC-ORD is the great documentation that is almost like a course in multivariate stats built in to the software. You can find answers to many of the common questions or even detailed descriptions regarding most of the algorithms used in the software, all of which has citations to primary literature for the even more curious user.
Canonical Correspondence Analysis (CCA)
CCA is unique among the ordination methods in PC-ORD in that the ordination of the main matrix (by reciprocal averaging) is constrained by a multiple regression on variables included in the second matrix. In community ecology, this means that the ordination of samples and species is constrained by their relationships to environmental variables. CCA is most likely to be useful when: (1) species responses are unimodal (hump-shaped), and (2) the important underlying environmental variables have been measured.
Comprehensive Includes major analysis tools plus unique tools for community analyses
Detrended Correspondence Analysis (DCA, DECORANA)
DCA is an eigenanalysis ordination technique based on reciprocal averaging (RA; Hill 1973). DCA is geared to ecological data sets and the terminology is based on samples and species. DCA ordinates both species and samples simultaneously.
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