NTSYSpc, Numerical Taxonomy System, Version 2.2
Scientific software for teaching and research
Windows 98/2000/NT/XP
NTSYSpc can be used to discover pattern and structure in multivariate data. The input can be descriptive information about collections of objects or directly measured similarities or dissimilarities between all pairs of objects. NTSYSpc can transform data, prepare summary of relationship, ordination, multifactor analyses. The program takes advantage of the Windows environment and allows long file names and the processing of large datasets. Plot options windows allow you to customize the plots.
Summary Of Features In NTSYSpc Version 2.2
» Similarity and dissimilarity : correlation, distance, 34 association coefficients, and 11 genetic distance coefficients.
» Clustering : UPGMA and other hierarchical SAHN methods (allows for ties). Neighbor-joining method (including the new unweighted version). Several types of consensus trees.
» Graph theoretic methods : minimum-length spanning trees. Graphs (unrooted trees) from the neighbor-joining method
» Ordination : principal components & principal coordinate’s analysis, correspondence analysis, metric & non-metric multidimensional scaling analysis, singular-value decompositions, projections onto axes and Burnaby's method etc.
» Interactive graphics : phenograms, phylogenetic trees, 2D scatter plots, comparison of dis/similarity matrices, Fourier plots of outlines, Procrustes plots, and 3-D perspective plots.
» Multivariate tests : canonical variates analysis, tests for homogeneity of covariance matrices, tests for number of dimensions, generalized multivariate multiple regression analysis. There are also provisions for bootstrap, jackknife, and simulation experiments.
» Geometric morph metrics : includes specialized modules for Procrustes analysis to superimpose landmark configurations, plotting the results of a Procrustes analysis, Fourier analysis and computation of 2D and 3D partial warp scores.
» Other : includes comparison of matrices by cophenetic correlation, Mantel test, data standardization, and matrix transformation.
» COMBINE : Combines two or more matrices into one.
» FACTOR : Performs the initial step for a factor analysis of a correlation or a covariance matrix.
» FROTATE : Performs the orthogonal or oblique factor rotation step in a factor analysis.
» FSCORES : Computes factor scores. The Anderson-Rubin, Bartlett, Least-squares, Regression, multigroup, and Thompson methods are included.
» RESAMPLE : Create samples using bootstrap, jackknife, random permutation, or random normal deviates.
» SPLIT : Divides a matrix into two or more matrices.
» SUMMARY : Summarizes results of a resampling experiment (bootstrap, jackknife, etc.)
» CPCA : The output of the statistical tests are now better organized.
» POOLVCV : Added tests for homogeneity of subsets of covariance matrices.
» TPSWTS : New methods added for the computation of the uniform component.
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