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Technical details about NTSYSpc ver. 2.0

Computational modules

bulletCANPLS - Performs canonical correlation and partial least-squares analyses. Used to study correlations between two sets of variables.
bulletCONSEN - Computes a consensus tree for two of two or more trees (such as multiple tied trees from SAHN or between two different methods). Several consensus indices are also computed to measure the degree of agreement between trees.
bulletCOPH - Produces a cophenetic value matrix (matrix of ultrametric values) from a tree matrix (produced, e.g., by the SAHN program). This matrix can be used by the MXCOMP program to test the goodness of fit of a cluster analysis to the similarity or dissimilarity matrix on which it was based.
bulletCORRESP - Correspondence analysis. This is a useful way to investigate the structure of 2-way contingency table.
bulletCPCA - Common principal components analysis of Flury (1984, 1988). Fits a single set of eigenvectors to a series of variance-covariance matrices.
bulletCVA - Performs a canonical vectors analysis (a generalization of discriminant function analysis). Can also test closeness of each specimen to each group mean.
bulletDCENTER - Performs a "double-centering" of a matrix of similarities or dissimilarities among the objects. The resulting matrix can then be factored to perform a principal coordinates analysis (a method for displaying relationships among objects in terms of their positions along a set of axes based on a dissimilarity matrix, see Gower 1966).
bulletEIGEN - Computes eigenvector and eigenvalue matrices of a real symmetric similarity matrix. This program can be used to perform a principal components or a principal coordinates analysis by extracting eigenvectors (factors) from a correlation or variance-covariance matrix.
bulletFOURIER - Computes Fourier and elliptic Fourier transformations and their inverses. Can be used on both 2D and 3D outline curves.
bulletMDSCALE - Nonmetric and linear multidimensional scaling analysis. This can be used as an alternative to PCA.
bulletMOD3D - Plots a 3-way scatter diagram as a 3-D perspective view of a model with n "objects" at tops of wires attached from a base plane. The view can be rotated interactively. This program is often used to view the results of a principal components or principal coordinates analysis.
bulletMST - Computes a minimum-length spanning tree from a similarity or dissimilarity matrix. This is useful for showing the nearest neighbors of objects based on their positions in a multidimensional space.
bulletMXCOMP - Compares two symmetric matrices by computing their matrix correlation and then plotting a scatter diagram. The statistics for a 2-way Mantel test are also computed. It can be used to compute the goodness of fit of a cluster analysis to a dataset (by comparing a cophenetic value matrix with a dissimilarity matrix).
bulletMXPLOT - Plots 2-way scatter diagrams of rows or columns of a matrix.
bulletNJOIN - Computes Saitou and Nei's (1987) neighbor-joining method trees as estimated phylogenetic trees.
bulletOUTPUT - Formats matrices into pages for printing. Results can be pasted into most word processors. This formatted output is also useful for checking to make sure that an input file has been prepared in the correct format for NTSYSpc.
bulletPOOLVC - Computes a pooled within-groups variance-covariance matrix from two or more data matrices. Can also perform a test for homogeneity of covariance matrices.
bulletPROJ - Projects a set of objects onto one or more vectors-or onto a space orthogonal to a set of vectors. In principal components analysis one will project standardized data onto the eigenvectors of the correlation matrix in order to see the best (in a least-squares sense) low-dimensional view of a data set. The orthogonal projection option can be used to implement Burnaby's (1966) method for size adjustment.
bulletSAHN - Performs the sequential, agglomerative, hierarchical, and nested clustering methods as defined by Sneath and Sokal (1973) . These include such commonly used hierarchical clustering methods as listed below. The program can find alternative trees when there are ties in the input matrix.
bulletcomplete-link (maximum method)
bulletsingle-link (minimum method)
bulletflexible clustering
bulletUPGMA (unweighted pair-group method)
bulletWPGMA (weighted pair-group method)
bulletWPGM using centroid clustering (either similarities or dissimilarities)
bulletWPGM using Spearman's average
bulletSIMGEND - Computes matrices of genetic distance coefficients from gene-frequency and DNA sequence data. The following coefficients can be selected.
bulletCavalli-Sforza and Edwards (1967) arc distance
bulletBalakrishnan and Sanghvi (1968) distance.
bulletCavalli-Sforza and Edwards (1967) chord distance.
bulletHillis (1984) distance
bulletSwofford and Olsen's (1990) suggestion to unbiass the distance by using same correction as in Nei's distance.
bulletNei's (1972) distance (default).
bulletNei's (1978) unbiased distance. Formula as above but with denominator:
bulletPrevosti (Wright, 1978 ) distance.
bulletRogers (1972) distance
bulletRogers distance as modified by Wright (1978
bulletJukes and Cantor (1969) distance modified for DNA sequence data.
bulletSIMINT - Computes various similarity or dissimilarity indices for interval measure (continuous) data (e.g., correlation, distance, etc. coefficients).
bulletBray-Curtis distance
bulletCanberra metric
bulletChi-squared distance
bulletAverage taxonomic distance
bulletSquared average distance
bulletEuclidean distance
bulletEuclidean distance squared
bulletManhattan distance
bulletPenrose's shape coefficient
bulletPenrose's size coefficient
bulletProduct-moment correlation
bulletCosine of angle
bulletSample size
bulletMorisita (1959) index
bulletHorn's (1966) modification of Morisita index
bulletRenkonen (1938) similarity
bulletVariances and covariances
bulletInner product
bulletSIMQUAL - Computes various association coefficients for qualitative data- data with unordered states (e.g., simple matching, Jaccard, phi, etc. coefficients). Hamann (1961) coefficient
bulletRogers and Tanimoto (1960) distance
bulletSimple matching coefficient
bulletDice (1945) coefficient
bulletJaccard (1908) coefficient
bulletKulcznski (1927) coefficients 1 and 2
bulletPhi coefficient
bulletRussel and Rao (1940) coefficient
bulletOchiai coefficient
bulletYule (1911) coefficient
bulletalso several unnamed coefficients from Sokal and Sneath (1961)
bulletSTAND - Performs a linear transformation of a data matrix so as to eliminate the effects of different scales of measurement. Several options for what gets subtracted off and what gets used as a divisor.
bulletSVD - Computes a singular-value decomposition of a rectangular matrix. It allows you to compute principal axes and projections in a single step.
bulletTPSWTS - Computes projections of the 2D or 3D coordinates of objects onto the principal warps of a thin-plate spline bending energy matrix (see Bookstein, 1991). This is done to enable a statistical analysis of the components of shape variation.
bulletTRANSF - Performs various linear and non-linear transformations of the rows or columns of a matrix. Computes Bookstein shape coordinates (both scaled and unscaled). Can also be used to delete rows or columns and alter the form of storage of some matrices.
bulletTREE - Displays a tree (e.g., from a cluster analysis) as a phenogram. Options are provided for scaling and scrolling through a tree interactively.

This file was last modified on Oct. 19, 1999.

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