Computing and Visualizing Log-linear analysis interactively

The purpose of this paper is to describe a simple program for computing log-linear analysis based on a direct manipulation interface that emphasizes the use of plots for guiding the analysis and evaluating the results obtained. The program described here works as a plugin for ViSta (Young 1997) and receives the name of LoginViSta (for Log-linear analysis in ViSTa). ViSta is a statistical package based on Lisp-Stat. Lisp-Stat is a statistical programming environment developed by Luke Tierney (1990) that features an object-oriented approach for statistical computing and one that allows for The purpose of this paper is to describe a simple program for computing log-linear analysis based on a direct manipulation interface that emphasizes the use of plots for guiding the analysis and evaluating the results obtained. The program described here works as a plugin for ViSta (Young 1997) and receives the name of LoginViSta (for Log-linear analysis in ViSTa). ViSta is a statistical package based on Lisp-Stat. Lisp-Stat is a statistical programming environment developed by Luke Tierney (1990) that features an object-oriented approach for statistical computing and one that allows for Computing and Visualizing Pedro Valero-Mora and Forrest W. Young interactive and dynamic graphs

The Strucplot Framework: Visualizing Multi-way

This paper describes the “strucplot” framework for the visualization of multi-way contingency tables. Strucplot displays include hierarchical conditional plots such as mosaic, association, and sieve plots, and can be combined into more complex, specialized plots for visualizing conditional independence, GLMs, and the results of independence tests. The framework’s modular design allows flexible customization of the plots’ graphical appearance, including shading, labeling, spacing, and legend, by means of “graphical appearance control” functions. The framework is provided by the R package vcd

MUVTIME: a Multivariate time series visualizer for behavioral science

As behavioral science becomes progressively more data driven, the need is increasing for appropriate tools for visual exploration and analysis of large datasets, often formed by multivariate time series. This paper describes MUVTIME, a multimodal time series visualization tool, developed in Matlab that allows a user to load a time series collection (a multivariate time series dataset) and an associated video. The user can plot several time series on MUVTIME and use one of them to do brushing on the displayed data, i.e. select a time range dynamically and have it updated on the display. The tool also features a categorical visualization of two binary time series that works as a high-level descriptor of the coordination between two interacting partners. The paper reports the successful use of MUVTIME under the scope of project TURNTAKE, which was intended to contribute to the improvement of human-robot interaction systems by studying turn- taking dynamics (role interchange) in parent-child dyads during joint action.Marie Curie International Incoming Fellowship PIIF-GA-2011- 301155; Portuguese Foundation for Science and Technology (FCT) project PTDC/PSI- PCO/121494/2010; AFP was also partially funded by the FCT project (IF/00217/2013)This research was supported by: Marie Curie International Incoming Fellowship PIIF-GA-2011301155; Portuguese Foundation for Science and Technology (FCT) Strategic program FCT UID/EEA/00066/2013; FCT project PTDC/PSIPCO/121494/2010. AFP was also partially funded by the FCT project (IF/00217/2013). REFERENCE

A Plot for the Visualization of Missing Value Patterns in Multivariate Data.

Missing data patterns are the combinations in which the variables with missing values occur. Exploring these patterns in multivariate data can be very useful but there are few specialized tools. The current paper presents a plot that includes relevant information for visualizing these patterns. The plot is also dynamic-interactive; so, selecting elements in it permits the highlighting of those that are more relevant according to certain criteria. An example, based on college data, is used for the purposes of illustrating the capabilities of the plo

Interactive Biplot Construction

We analyze and discuss how a generic software to produce biplot graphs should be designed. We describe a data structure appropriate to include the biplot description and we specify the algorithm(s) to be used for several biplot types. We discuss the options the software should offer to the user in two different environments. In a highly interactive environment the user should be able to specify many graphical options and also to change them using the usual interactive tools. The resulting graph needs to be available in several formats, including high quality format for printing. In a web-based environment, the user submits a data file or listing together with some options specified either in a file or using a form. Then the graphic is sent back to the user in one of several possible formats according to the specifications. We review some of the already available software and we present an implementation based in XLISP-STAT. It can be run under Unix or Windows, and it is also part of a service that provides biplot graphs through the web.

Princípios básicos na confecção de gráficos

Data visualization is a very important aspect of data analysis and of presentation. Focusing on the latter, this paper discusses various elements of constructing graphs for publications. Bad and good graphs are compared, and a checklist with graphical elements to be used while creating graphs is proposed.A visualização de dados é um aspecto importante da análise de dados e de sua apresentação. Nesse ponto de vista enfocam-se vários elementos da construção de gráficos para publicações. Gráficos ruins e bons são comparados, e uma lista de checagem de elementos de composição gráfica é proposta para ser utilizada durante a confecção de gráficos

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

Using Metaphors in Dynamic Social Stratification Visualizations

We present three information visualizations for studying social stratification. Each Web-based applet uses a different metaphor to display U.S. Census income data along with the categories of race, marital status, and profession. Each system is completely dynamic, affording the user the choice of categorical variable to compare, as well as the choice of categories within each visualization. Two different user interfaces have also been implemented. The systems are described, compared, and their respective merits and deficiencies discussed