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PC-ORD 规格Advisor Wizard(顾问向导) 导入/导出文件格式 图形
数据修改
Ordinations
Groups
Traits 主要统计包中不可用的功能 顾问向导 通过问答对话，PC-ORD 使用决策树来帮助您决定如何转换和分析数据。 您也可以将其用作自学工具。 社区分析海报的决策树现已可用。 图表 出版质量的图形可以打印、保存到文件或粘贴到其他应用程序中。 可以使用各种叠加，包括不同的符号大小、标签、矢量、网格和联合图。 数据中的代码按颜色或符号类型分组。
图形格式
Ordinations Bray-Curtis (Polar) We offer numerous options and improvements beyond Bray and Curtis' original method, such as perpendicularized axes and variance-regression endpoint selection. 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. 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. Non-metric Multidimensional Scaling (NMS) Non-metric Multidimensional Scaling (NMS, MDS, NMDS, or NMMDS) is an ordination method that is well suited to data that are nonnormal or are on arbitrary, discontinuous, or otherwise questionable scales. NMS is generally the best ordination method for community data. Our auto-pilot feature makes it easy to use. A Monte Carlo test of significance is included. NMS Scores NMS Scores provides a prediction algorithm for non-metric multidimensional scaling (NMS). This is not prediction in the sense of forecasting, but rather statistical prediction in the same way as using multiple regression to estimate a dependent variable. NMS Scores calculates scores for new items based on prior ordinations. Principal Components Analysis (PCA) Principal Components Analysis is the basic eigenanalysis technique. It maximizes the variance explained by each successive axis. Although it has severe faults with many community data sets, it is probably the best technique to use when a data set approximates multivariate normality. PCA is usually a poor method for community data, but it is the best method for many other kinds of multivariate data. Broken-stick eigenvalues are provided to help you evaluate statistical significance. Principal Coordinates Analysis (PCoA) Principal Coordinates Analysis is an eigenanalysis technique similar to PCA, except that one extracts eigenvectors from a distance matrix among sample units (rows), rather than from a correlation or covariance matrix. In PCoA one can use any square symmetrical distance matrix, including semi-metrics such as Sorensen distance, as well as metric distance measures such as Euclidean distance. Reciprocal Averaging (RA) = Correspondence Analysis (CA) Reciprocal averaging is also known as correspondence analysis (CA). It is performed in PC-ORD by selecting options in program DCA adapted from the Cornell Ecology Program series. Reciprocal averaging (RA) yields both normal and transpose ordinations automatically. Like DCA, RA ordinates both species and samples simultaneously. 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. Weighted Averaging The simplest yet often effective method of ordination is weighted averaging. The essential operation is the same: a set of pre-assigned species weights (or weights for species groups) are used to calculate scores for sites (sample units). The calculation is a weighted averaging for species or species groups actually present in a sample unit. Weighted averaging used in Federal Manual and numerous ecological indices. Fuzzy Set (FSO) Fuzzy set ordination applies fuzzy set theory to direct gradient analysis in ecological ordination. This ordination method requires the user to hypothesize the relationship between species communities and environmental variables or other predictors. The predictors are most commonly environmental variables, but they can also be a secondary set of species communities, or any other quantitative data set with the same number of rows as the community matrix. The community data are placed in the main matrix, and the secondary set is in the second matrix. The resulting ordination is an ordination of sample units in species space. Species can be superimposed on the ordination by a single weighted averaging step Compare Scores (Compare Ordinations) Evaluate the similarity of two ordinations, independent of any rotation, reflection, units for axis, and number of dimensions. This is accomplished by evaluating the correlation between the interpoint distances of two ordinations. Squaring this correlation expresses the redundancy between two ordinations. A formal test of the hypothesis of no relationship between the two ordinations is provided by a Mantel test. Groups Cluster Analysis We offer eight fusion strategies and eight distance measures, for hierarchical, polythetic, agglomerative cluster analysis. Results are given for each step in the analysis, along with a publication-quality final dendrogram. Cluster Analysis graph example Two-way Cluster Analysis The purpose of our two-way clustering (also known as biclustering) is to graphically expose the relationship between cluster analyses and your individual data points. The resulting graph makes it easy to see similarities and differences between rows in the same group, rows in different groups, columns in the same group, and columns in different groups. You can see graphically how groups of rows and columns relate to each other. Two-way clustering refers to doing a cluster analysis on both the rows and columns of your matrix, followed by graphing the two dendrograms simultaneously, adjacent to a representation of your main matrix. Rows and columns of your main matrix are re-ordered to match the order of items in your dendrogram. Two-way Cluster Analysis graph example Group Linkage Methods
Ward's is also know as Orloci's and Minimum Variance Method Multi-Response Permutation Procedures (MRPP) MRPP is a non-parametric procedure for testing the hypothesis of no difference between two or more groups of entities. The groups must be a priori. For example, one could compare species composition between burned and unburned plots to test the hypothesis of no treatment effect. Discriminant analysis is a parametric procedure that can be used on the same general class of questions. However, MRPP has the advantage of not requiring assumptions (such as multivariate normality and homogeneity of variances) that are seldom met with ecological community data. Eight distance measures options are available. Blocked Multi-Response Permutation Procedures (MRBP) Randomized block experiments or paired-sample data can be analyzed with a variant of MRPP called MRBP or blocked MRPP. PC-ORD allows up to 1000 blocks and 100 groups. Indicator Species Analysis Dufrêne and Legendre’s (1997) method provides a simple, intuitive solution to the problem of evaluating species associated with groups of sample units. It combines information on the concentration of species abundance in a particular group and the faithfulness of occurrence of a species in a particular group. It produces indicator values for each species in each group. These are tested for statistical significance using a Monte Carlo technique. Blocked Indicator Species Analysis Dufrêne and Legendre’s (1997) method for Indicator Species Analysis can be adapted to a randomized block experiment or a paired-sample design. The data are pre-relativized by species within blocks (or pairs), such that the sum across groups equals one for each block. If a species is absent from a block, the abundances are maintained at zero. The relativization alters the relative abundance portion of the Indicator Value (IV) index to focus on within block differences. Then the ISA is run as usual. The randomization test differs from regular ISA in that instead of an unconstrained permutation of group identifiers, groups are randomly permuted within blocks. Phi Coefficient for Indicator Species Tichy and Chytry's (2006) phi coefficient is a method for evaluating the indicator value (or diagnostic value) of a species with respect to a one-way grouping of sample units. It applies only to presence-absence data. If have quantitative data you choose this option in the Indicator Species Analysis Setup, then the data are automatically converted to presence-absence. Any value greater than zero is transformed to 1, while values less than or equal to zero are transformed to zero. Tichy and Chytry's method corrects for unequal sample sizes among groups. The adjusted phi coefficient also allows comparisons across studies with different sample sizes. Mantel Test The Mantel test evaluates the null hypothesis of no relationship between two dissimilarity (distance) or similarity matrices. The Mantel test is an alternative to regressing distance matrices that circumvents the problem of partial dependence in these matrices. Example applications are: evaluating the correspondence between two groups of organisms from the same set of sample units or comparing community structure before and after a disturbance. Two methods are available in PC-ORD: Mantel’s asymptotic approximation and a randomization (Monte Carlo) method. Partial Mantel Test The partial Mantel test requires three matrices, the main matrix, a second matrix, and a control matrix. The null hypothesis is of no relationship between the main and second matrices, after controlling for the relationship with the third (control) matrix. If we call the main matrix Y, the second matrix X, and the control matrix C, then we seek the partial correlation between X and Y while controlling for C. PerMANOVA PerMANOVA performs distance-based multivariate analysis of variance, also known as nonparametric MANOVA or npMANOVA. Hypothesis are evaluated with permutation tests, rather than by reference to an assumed distribution. Options include one-way, SumF A simple but surprisingly effective method of comparing two or more groups of sample units is to calculate a univariate F statistic for each variable, sum those F statistics, then compare the resulting sum to the distribution of F statistics based on randomizing the data under the null hypothesis. This is the core of the SumF method, as suggested by Edginton (1995). Good performance of this method, as compared to distance-based methods, was found by Warton and Hudson (2004). An advantage to this method is that by aggregating a simple, well-known test statistic, the F ratio, into a summary statistic across multiple variables, we simultaneously obtain information about differences between groups both across all variables and for individual variables. Thus for the generic question, "Do communities differ between groups?", the SumF method allows us to report an answer for communities as a whole as well as for individual species. TWINSPAN TWINSPAN simultaneously classifies species and samples. At its core, TWINSPAN is based on dividing a reciprocal averaging ordination space. One of the most useful features of TWINSPAN is the final ordered two-way table. Species names are arrayed along the left side of the table, while sample numbers are along the top. The pattern of zeros and ones on the right and bottom sides define the dendrogram of the classifications of species and samples, respectively. The interior of the table contains the abundance class of each species in each sample. Abundance classes are defined by pseudospecies cut levels. 导入/导出文件格式
数据修改 转换
Relativizations
Traits PC-ORD 7 provides ways to relate data on species traits (trait matrix) to community samples (main matrix) and environmental data (second matrix). While many of these operations can be done in the other PC-ORD menu items, the Traits menu provides several operations specific to this kind of data. Traits | Categorical to Binary Traits | Create Trait Combinations For example, say you had two categorical variables, one coding for native vs. non-native species, and one coding for annuals vs. perennials. That might work well in the analyses, but what if species having a combinations of those, for example the non-native annuals, is particularly different ecologically from all remaining species? You might, therefore, wish to create a new categorical variable with all four combinations of those trait categories: (1) native annuals, (2) native perennials, (3) non-native annuals, (4) non-native perennials. Traits | Calculate SU x Traits Matrix Traits | Species Distances in Trait Space Traits | Functional Diversity Traits | Fourth Corner Analysis Fuzzy Set (FSO) Summaries Descriptive Statistics and Diversity Indices Outlier Analysis Species-area Curves Species Lists Write Distance Matrix Shuffle 距离测量
矩阵操作
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