1、often called the dominant or leading eigenvalue. Eigenvalues are also often called latent values. 4) The eigenvalue is a measure of the strength of an axis, the amount of variation along an axis, and ideally the importance of an ecological gradient. The precise meaning depends on the ordination meth
2、od used. 5) The eigenvectors are the sample scores, if the rows and columns of the initial matrix represent samples. 6) Some texts (e.g. Digby and Kempton 1987) describe singular value decomposition 8 instead of eigenanalysis. The approaches are theoretically identical. 7) Principal Components Analy
3、sis, Correspondence Analysis (Reciprocal Averaging), DCA (Detrended Correspondence Analysis) are all examples of eigenanalysis-based approaches. Principal Components Analysis (PCA) PCA is the basic eigenanalysis technique. It was first used in ecology by Goodall (1954). It maximizes the variance exp
4、lained 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 available in many statistical packages. PCA is often considered a form of factor analysis. PCA is a rigid
5、rotation of the data matrix: it does not change the positions of points relative to each other; it just changes the axis systems. PCA is mathematically quite elegant. In PCA, the perpendicular distance from each object to the ordination axes is minimized. Axes are linear combinations of species/vari
6、ables. The weights are known as coefficients or loadings. PCA is good for data which are not in same units, in which case data must be standardized to zero mean and unit variance (this is known as PCA on the correlation matrix). PCA can accept negative numbers for variables/species. PCA can place ne
7、w points in an old ordination. PCA can find characteristics of any point in ordination space (though this is not standard in most packages). Eigenvalues have a meaning: variance explained. The sum of the eigenvalues will equal the sum of the variance of all variables. If performed on a correlation m
8、atrix, the sum of the eigenvalues will equal the number of variables/species. PCA has a serious problem for vegetation data: the horseshoe effect. This is caused by the curvilinearity of species distributions along gradients. the most widely used ordination method a linear model (the coordinates of
9、an SU in the space of the PCA axes system are determined by a linear combination of weighted species abundances) Statistical packages: MVSP, NTSYS, PC-ORD, PRIMER, SAS, SYN-TAX, and SYSTAT 9 10 Correspondence Analysis (CA) or Reciprocal Averaging (RA) Reciprocal Averaging means that sample scores ar
10、e calculated as a weighted average of species scores, and species scores are calculated as a weighted average of sample scores, and iterations continue until there is no change. However, other algorithms are possible. CA simultaneously arr距离应符合以下要求：外电线路电压为1kV以下时，最小垂直距离为6m；外电线路电压为135kV时，最小垂直距离为7m。 4）起重机严禁越过无防护设施的外电架空线路作业。在外电架空线路附件吊装时，起重机的任何部位或被吊物的边缘在最大偏斜时与架空线路边线的最小安全距离应符合以下要求：外电线路电压为1kV以下时，最小水平与垂直距离为1.5m；外电线路电压为10kV以下时，最小垂直距离为3m，水平距离为2m；外电线路电压为35kV以下时，最小垂直距离为4m，水平距离为3.5m；外电线路电压为110kV以下时，最小垂直距离为5m，水平距离为4m；外电线路电压为220kV以下时，最小水平与垂直距离为6m；外电线路电压为330kV以下时，最小水平与垂直距离为7m；外电线路电压为500kV以下
