The R2WinBUGS package provides convenient functions to call WinBUGS from R. It automatically writes the data and scripts in a format readable by WinBUGS for processing in batch mode, which is possible since version 1.4. After the WinBUGS process has finished, it is possible either to read the resulting data into R by the package itself-which gives a compact graphical summary of inference and convergence diagnostics-or to use the facilities of the coda package for further analyses of the output. Examples are given to demonstrate the usage of this package.
Scatterplot3d is an R package for the visualization of multivariate data in a three dimensional space. R is a "language for data analysis and graphics". In this paper we discuss the features of the package. It is designed by exclusively making use of already existing functions of R and its graphics system and thus shows the extensibility of the R graphics system. Additionally some examples on generated and real world data are provided.
The unrehearsed performance of music, called ‘sight-reading’ (SR), is a basic skill for all musicians. It is of particular interest for musical occupations such as the piano accompanist, the conductor, or the correpetiteur. However, up until now, there is no theory of SR which considers all relevant factors such as practice-related variables (e.g. expertise), speed of information processing (e.g. mental speed), or psychomotor speed (e.g. speed of trills). Despite the merits of expertise theory, there is no comprehensive model that can classify subjects into high- and low-performance groups. In contrast to previous studies, this study uses a data mining approach instead of regression analysis and tries to classify subjects into predetermined achievement classes. It is based on an extensive experiment in which the total SR performance of 52 piano students at a German music department was measured by use of an accompanying task. Additionally, subjects completed a set of psychological tests, such as tests of mental speed, reaction time, working memory, inner hearing, etc., which were found in earlier studies to be useful predictors of SR achievement. For the first time, classification methods (cluster analysis, regression trees, classification trees, linear discriminant analysis) were applied to determine combinations of variables for classification. Results of a linear discriminant analysis revealed a two-class solution with four predictors (cross-validated error: 15%) and a three-class solution with five predictors (cross-validated error: 33%).
Since a few years, classification in music research is a very broad and quickly growing field. Most important for adequate classification is the knowledge of adequate observable or deduced features on the basis of which meaningful groups or classes can be distinguished. Unsupervised classification additionally needs an adequate similarity or distance measure grouping is to be based upon. Evaluation of supervised learning is typically based on the error rates of the classification rules. In this paper we first discuss typical problems and possible influential features derived from signal analysis, mental mechanisms or concepts, and compositional structure. Then, we present typical solutions of such tasks related to music research, namely for organization of music collections, transcription of music signals, cognitive psychology of music, and compositional structure analysis.
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