Abstract: QQ-plots are extremely useful in univariate data analysis. In this article, Koltchinskii (1997) and Chaudhuri’s (1996) definition of multivariate quantile is used to develop analogous plots for bivariate data. Bivariate qq-plots are exhibited for comparing a sample to a given population distribution (the bivariate normal), and for comparing two or more bivariate samples. The plots are...
Simon J. Walton,
Anne E. Trefethen,
In this paper, we present a novel visualization technique for assisting in observation and analysis of algorithmic complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It sup...
For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with ...
Bruce J. Swihart,
Bryan D. James,
Brian S. Schwartz,
Naresh M. Punjabi,
Longitudinal repeated-measures data have often been visualized with spaghetti plots for continuous outcomes. For large datasets, the use of spaghetti plots often leads to the over-plotting and consequential obscuring of trends in the data. This obscuring of trends is primarily due to overlapping of trajectories. Here, we suggest a framework called lasagna plotting that constrains the subject-sp...
Bruce J Swihart,
Bryan D James,
Brian S Schwartz,
Naresh M Punjabi,
Longitudinal repeated measures data has often been visualized with spaghetti plots for continuous outcomes. For large datasets, this often leads to over-plotting and consequential obscuring of trends in the data. This is primarily due to overlapping of trajectories. Here, we suggest a framework called lasagna plotting that constrains the subject-specific trajectories to prevent overlapping and ...
In Multiple linear regression models, problems arise when serious multicollinearity or influential outliers are present in the data. Failure to include significant quadratic or cross-product terms result in model specification error. Simple scatter plots are most of the time not effective in revealing the complex relationships of predictor variables or data problems in multiple linear regressio...
Dot plots are a standard method for local comparison of biological sequences. In a dot plot, a substring to substring distance is computed for all pairs of fixed-size windows in the input strings. Commonly, the Hamming distance is used since it can be computed in linear time. However, the Hamming distance is a rather crude measure of string similarity, and using an alignment-based edit distance...
:Physical review. E, Statistical, nonlinear, and soft matter physics2006
D B Vasconcelos,
S R Lopes,
R L Viana,
We propose an extension of the recurrence plot concept to perform quantitative analyzes of roughness and disorder of spatial patterns at a fixed time. We introduce spatial recurrence plots (SRPs) as a graphical representation of the pointwise correlation matrix, in terms of a two-dimensional spatial return plot. This technique is applied to the study of complex patterns generated by coupled map...
Error-return plots show the rate of error (misunderstanding) against the rate of nonreturn (non-understanding) for Natural Language Processing systems. They are a useful visual tool for judging system performance when other measures such as recall/precision and detection-error tradeoff are less informative, specifically when a system is judged on the correctness of its responses, but may elect ...