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Canoco是一套在生態(tài)學及幾個相關(guān)領(lǐng)域內(nèi)使用排序方法來進行多變量統(tǒng)計分析的常用程序包。 Canoco 5是一套全新的、幾乎完全重構(gòu)的Canoco軟件，發(fā)布于2012年十月，是生態(tài)學應(yīng)用軟件中用于約束與非約束排序的流行工具。
Canoco 5合了排序以及回歸和排列方法學，以便得到全面的生態(tài)數(shù)據(jù)統(tǒng)計模型。Canoco 5包括線性和曲線單峰方法。使用Canoco 5進行排序，能夠了解：
生物群落的結(jié)構(gòu)
植物與動物群落以及它們的環(huán)境之間的聯(lián)系
一個對環(huán)境和(或)其生物群落的假設(shè)沖擊所能造成的影響
在生物群落上進行的複雜生態(tài)學和生態(tài)毒理學實驗的相關(guān)處理所能造成的影響
軟件特色
Canoco 5實現(xiàn)了在排版方面的進展，例如變化分區(qū)，協(xié)同對應(yīng)分析和基于距離的冗余分析，但主要的進展是用戶的友好性。在Canoco 5中，數(shù)據(jù)導(dǎo)入，分析和繪制圖形被集成到一個Canoco 5項目中。
Canoco幫助選擇數(shù)據(jù)轉(zhuǎn)換和分析方法。過去需要多次運行的數(shù)值分析，現(xiàn)在可以通過單峰分析模板和分析筆記簡潔地總結(jié)結(jié)果，并允許訪問完整的結(jié)果。
所有對一組數(shù)據(jù)表示所做的分析現(xiàn)在都在Canoco 5項目中手機，共享分析和繪圖設(shè)置。Canoco 5有助于制作更好的發(fā)布質(zhì)量排序圖。
手冊已經(jīng)被重新編寫，大量的現(xiàn)實生活中的粒子被更新和擴展，以展示處理多變量數(shù)據(jù)的新方法。
軟件功能
對兩組或三組預(yù)測者來說，變化劃分很容易實現(xiàn)，包括根據(jù)部分或非部分分析和使用原始或調(diào)整的變化估計計算被解釋的變化的各個部分。
在變分劃分框架中，可以使用鄰矩陣主坐標（PCNM）方法。目前的實現(xiàn)符合Legendre&Legendre（2012）中的另一個方法名稱（dbMEM）下的建議。
計算、測試和繪制主響應(yīng)曲線（PRC）現(xiàn)在是一項簡單的任務(wù)。
工隊應(yīng)分析（CoCA，對稱形式）可用，包括蒙特卡羅置換測試兩種比較群落類型的共變異。
預(yù)測器的逐步選擇在視覺上得到了增強，現(xiàn)在提供了對I型錯誤膨脹的保護（用所有預(yù)測器進行初步測試，通過三種方法之一調(diào)整P值：錯誤發(fā)現(xiàn)率（FDR）估計、Holm校正和Bonferroin校正）。
可以直接測試所有約束軸，并將兩個培訓結(jié)果與Procrustes分析進行比較
您可以輕松地處理物種的功能特性或?qū)胛锓N的系統(tǒng)發(fā)育相關(guān)數(shù)據(jù)，以及計算和使用功能多樣性。
可視化功能得到了增強，例如半透明填充色，在縱坐標圖中顯示變量箭頭的校準軸或繪制封閉橢圓以替代封閉多邊形。在現(xiàn)有文件格式（PNG、BMP 、EMF、Adobe Illustrator）中添加了JPEG、TIFF和PDF文件格斯的其他導(dǎo)出類型。
您的工作的每一步都由上下文敏感的幫助系統(tǒng)和Cancona支持 - 一個專家系統(tǒng)，幫助您為那您的研究問題Corre選擇合適的分析方法。
操作系統(tǒng)：
Canoco在Windows 8 、 8.1或10的32位和64位版本的標準臺式機和筆記本電腦上都可以正常工作。
Canoco 5也可以在其他Microsoft操作系統(tǒng)上運行，從安裝了（Service Pcak）2或SP3的Microsoft Windows XP開始。這也包括Windows Vista和Windows 7。
Canoco 5經(jīng)過測試，可以在Linux上的Wine環(huán)境下運行，并且也可以在CrossOver軟件包的類似環(huán)境中運行。
【英文介紹】
Canoco is one of the most popular programs for multivariate statistical analysis using ordination methods in the field of ecology and several related fields. User's Guides of the recent Canoco versions (4.0, 4.5 and 5.0) were cited more than 9200 times in the past 18 years (1999-2017, ISI Web of Knowledge).
Canoco 5 is the latest, much re-worked version of the Canoco software, released in October 2012.
The main features of the Canoco 5 program are summarized in the following points.
Analytical and graphing capabilities are integrated with an easy-to-use spreadsheet data editor in a single program. All analyses done on a set of data tables are now collected within a Canoco 5 project, sharing the analytical and graphing settings.
All statistical methods offered by Canoco for Windows 4.5 are available, such as DCA, CA, CCA, DCCA, PCA, and RDA methods - including their partial variants, with Monte Carlo permutation tests for constrained ordination methods, offering appropriate permutation setup for data coming from non-trivial sampling designs.
For newly available methods see below.
All visualization tools offered by CanoDraw 4.x are available (including loess, GLM and GAM models for the visualization of data attributes in ordination space) and many of them are improved.
Data can be entered within the program itself or easily imported from Excel (.XLS or .XLSX formats) or from Canoco 4.x data files. Labels no longer need to be shortened to 8 characters, but these brief forms are still available for display in the ordination diagrams and can even be automatically generated from the long ones. Standard coding of factors (categorical predictors) is now used, dummy (0/1) variables are generated internally. The editor allows transformation from dummy variables to factors and, if needed, the reverse.
Principal coordinate analysis (PCoA) and distance based RDA (db-RDA) are now easily accessible, with new distance measures added (11 distance types in total, including Bray-Curtis, Gower distance, or Jaccard coefficients). Similarly, non-metric multidimensional scaling (nMDS) is also supported.
Variation partitioning is easily accessible for two or three groups of predictors including calculations of individual fractions of explained variation, based either on partial or non-partial analyses and using either raw or adjusted variation estimates.
Principal coordinates of neighbour matrices (PCNM) method is available within the variation partitioning framework. Present implementation matches the suggestions described in Legendre & Legendre (2012) under an alternative method name (dbMEM).
Computing, testing and graphing of the Principal Response Curves (PRC) is now an easy task.
Co-correspondence analysis (CoCA, symmetric form) is available, including Monte Carlo permutation testing of the covariation among the two compared community types.
Stepwise selection of predictors was visually enhanced and provides now the support for protection against Type I error inflation (preliminary test with all predictors and the adjustment of p values by one of three methods: false discovery rate (FDR) estimates, Holm correction, and Bonferroni correction.
Straightforward testing of all constrained axes as well as comparing results of two ordinations with Procrustes analysis is available.
You can easily work with species functional traits or import the data on phylogenetic relatedness of species, as well as calculate and use functional diversity.
Visualization capabilities were enhanced with features such as the semi-transparent fill colours, displaying calibration axes for variable' arrows in ordination diagrams, or plotting enclosing ellipses as an alternative to enclosing polygons. Additional types of export in JPEG, TIFF, and PDF file formats were added to existing ones (PNG, BMP, EMF, Adobe Illustrator).
Every step of your work is supported by an context-sensitive help system and by the Canoco Adviser – an expert system that helps you to select a proper analytical method for your research question, correct type of ordination model (linear vs. unimodal), data transformation, or appropriate visualization of the results. It even advises you how to interpret ordination diagrams you create with the help of Graph Wizard.
Advanced users can combine multiple methods in a single analysis, including generalized linear models to correlate scores etc.
