使用期限*
许可形式单机版
原产地美国
介质下载
适用平台Windows
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Multivariate Analysis of Ecological Data For Windows 98, 00, ME, NT, XP, Vista, 7, 8, and 10
PC-ORD performs multivariate analysis of ecological data entered in spreadsheets. Our emphasis is on nonparametric tools, graphical representation, randomization tests, and bootstrapped confidence intervals for analysis of community data. In addition to utilities for transforming data and managing files, PC-ORD offers many ordination and classification techniques not available in major statistical packages including: CCA, DCA, Indicator Species Analysis, Mantel tests and partial Mantel tests, MRPP, PCoA, perMANOVA, RDA, two-way clustering, TWINSPAN, Beals smoothing, diversity indices, species lists, many ordination overlay methods (quantitative, symbol-coding, color-coding, grid, joint plot, biplot, successional vector), various rotation methods, 3-D ordination graphics, Bray-Curtis ordination, city-block distance measures, species-area curves, tree data summaries, publication-quality dendrograms, and autopilot mode nonmetric multidimensional scaling (NMS or NMDS). Very large data sets can be analyzed. Most operations accept a matrix up to 32,000 rows or 32,000 columns and up to 536,848,900 matrix elements, provided that you have adequate memory in your computer. The terminology is tailored for ecologists. The full manual is included as a context-sensitive help system.
- Convex Hulls Filled Polygons
A convex hull is an overlay that uses a polygon to enclose all of the points in a group.
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Functional Diversity
Functional diversity分析了样本单元x物种矩阵与物种x性状矩阵的组合。PC-ORD中的功能多样性措施的原理和使用在以下主题中描述。
Redundancy Analysis (RDA)
Redundancy Analysis models a set of response variables as a function of a set of predictor variables, based on a linear model. RDA thus applies to the same conceptual problem as canonical correspondence analysis (CCA). RDA is, however, based on a linear model among response variables and between response variables and predictors. CCA, on the other hand, implies a unimodal response to the predictors.
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