Multivariate Methods of Representing Relations in R for Prioritization Purposes [electronic resource] : Selective Scaling, Comparative Clustering, Collective Criteria and Sequenced Sets /
Myers, Wayne L.author.
Patil, Ganapati P.author.
This monograph is a four-fold featuring of adaptive analysis. · First is data distillation and comparative coupling whereby the results of one analysis are fed forward into another analysis without necessarily returning directly to the original data matrix, and analytical avenues usually seen as alternatives are pursued in parallel with results being carried forward together as complementary comparatives. · Second is the flexibility and suitability of the R© statistical software system for engaging in such adaptive and conjunctive statistical strategies.  The intention is to provide an extensive entry into the realms of R using exploration by example whereby a demonstrative dataset of manageably moderate size is carried comparatively though the sequence of sections. · Third is a major mission to introduce innovative methodologies for preliminary and/or partial prioritization that arise from partial order theory.  We formulate functions in R that provide for generation and visualization of partial orderings based on combinations of criteria.  These methods support etiological exploration for explanations that underlie apparent concurrence or conflict among multiple indicators of suitability or severity. Fourth is delving more deeply into some multivariate methods such as principal components using the matrix methods available in R.  R makes highly compact calls available for several such multivariate methods, but sometimes discernment demands delving into details.  .
Description based upon print version of record.
Includes bibliographical references and index.
Motivation and Computation -- Part I: Synergistic Scalings, Contingent Clustering and Distance Domains -- Suites of Scalings -- Rotational Rescaling and Disposable Dimensions -- Comparative Clustering for Contingent Collectives -- Distance Domains, Skeletal Structures and Representative Ranks -- Part II: Precedence and Progressive Prioritization -- Ascribed Advantage, Subordination Schematic and ORDIT Ordering -- Precedence Plots, Coordinated Crite4ria and Rank Relations -- Case Comparisons and Precedence Pools -- Distal Data and Indicator Interactions -- Landscape Linkage for Prioritizing Proximate Patches -- Constellations of Criteria -- Severity Setting for Human Health -- Part III: Transformation Techniques and Virtual Variates -- Matrix Methods for Multiple Measures -- Segregating Sets Along Directions of Discrimination -- Index.
This monograph is a four-fold featuring of adaptive analysis. · First is data distillation and comparative coupling whereby the results of one analysis are fed forward into another analysis without necessarily returning directly to the original data matrix, and analytical avenues usually seen as alternatives are pursued in parallel with results being carried forward together as complementary comparatives. · Second is the flexibility and suitability of the R© statistical software system for engaging in such adaptive and conjunctive statistical strategies.  The intention is to provide an extensive entry into the realms of R using exploration by example whereby a demonstrative dataset of manageably moderate size is carried comparatively though the sequence of sections. · Third is a major mission to introduce innovative methodologies for preliminary and/or partial prioritization that arise from partial order theory.  We formulate functions in R that provide for generation and visualization of partial orderings based on combinations of criteria.  These methods support etiological exploration for explanations that underlie apparent concurrence or conflict among multiple indicators of suitability or severity. Fourth is delving more deeply into some multivariate methods such as principal components using the matrix methods available in R.  R makes highly compact calls available for several such multivariate methods, but sometimes discernment demands delving into details.  .
Statistics .
R (Computer program language).
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Statistics for Life Sciences, Medicine, Health Sciences.