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AbstractRegression experts consistently recommend plotting residuals for model diagnosis, despite the availability of many numerical hypothesis test procedures designed to use residuals to assess problems with a model fit. Here we provide evidence for why this is good advice using data from a visual inference experiment. We show how conventional tests are too sensitive, which means that too often the conclusion would be that the model fit is inadequate. The experiment uses the lineup protocol which puts a residual plot in the context of null plots. This helps generate reliable and consistent reading of residual plots for better model diagnosis. It can also help in an obverse situation where a conventional test would fail to detect a problem with a model due to contaminated data. The lineup protocol also detects a range of departures from good residuals simultaneously. Supplemental materials for the article are available online. [ABSTRACT FROM AUTHOR]

Spatial distributions have been presented on alternative representations of geography, such as cartograms, for many years. In modern times, interactivity and animation have allowed alternative displays to play a larger role. Alternative representations have been popularised by online news sites, and digital atlases with a focus on public consumption. Applications are increasingly widespread, especially in the areas of disease mapping, and election results. The algorithm presented here creates a display that uses tessellated hexagons to represent a set of spatial polygons, and is implemented in the R package called sugarbag. It allocates these hexagons in a manner that preserves the spatial relationship of the geographic units, in light of their positions to points of interest. The display showcases spatial distributions, by emphasising the small geographical regions that are often difficult to locate on geographic maps. [ABSTRACT FROM AUTHOR]

Children and young people (CYP) can be empowered to take on roles as agents of change in their own communities. CYP want to be heard and should be actively involved in the co-production, design and development of services aimed at them to ensure that the resulting services are acceptable and accessible. Little analysis of the framing and discourse of co-production in different contexts has been undertaken. Building on Children's Advocacy Center models from the United States of America (which are held in high esteem by local communities), there is perceived value of such a center in the UK. A service development initiative was designed to work with children from Greater Manchester (UK) to determine the potential for the establishment of a children's advocacy center in the North of England. This report presents the design and outcome of the initiative and contributes to the literature on the co-production of such service development projects with CYP, notably the means of achieving that outcome. Recommendations are made for the piloting of an Advocacy House model in the UK with collaborative efforts between CYP as well as health, education, law enforcement, social care providers, charities and voluntary groups. A community-inclusive partnership, underpinned by the principles of co-production and co-design, is integral to the further development of this pilot. [ABSTRACT FROM AUTHOR]

Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense that the methodology is relevant today. This article describes the trials and tribulations of refreshing a textbook dataset on wages, extracted from the National Longitudinal Survey of Youth (NLSY79) in the early 1990s. The data is useful for teaching modeling and exploratory analysis of longitudinal data. Subsets of NLSY79, including the wages data, can be found in supplementary materials from numerous textbooks and research articles. The NLSY79 database has been continually updated through to 2018, so new records are available. Here we describe our journey to refresh the wages data, and document the process so that the data can be regularly updated into the future. Our journey was difficult because the steps and decisions taken to get from the raw data to the wages textbook subset have not been clearly articulated. We have been diligent to provide a reproducible workflow for others to follow, which also hopefully inspires more attempts at refreshing data for teaching. Three new datasets and the code to produce them are provided in the open source R package called yowie. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This article discusses a high‐dimensional visualization technique called the tour, which can be used to view data in more than three dimensions. We review the theory and history behind the technique, as well as modern software developments and applications of the tour that are being found across the sciences and machine learning. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional DataStatistical and Graphical Methods of Data Analysis > Statistical Graphics and VisualizationStatistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis [ABSTRACT FROM AUTHOR]

Multivariate data is often visualized using linear projections, produced by techniques such as principal component analysis, linear discriminant analysis, and projection pursuit. A problem with projections is that they obscure low and high density regions near the center of the distribution. Sections, or slices, can help to reveal them. This article develops a section pursuit method, building on the extensive work in projection pursuit, to search for interesting slices of the data. Linear projections are used to define sections of the parameter space, and to calculate interestingness by comparing the distribution of observations, inside and outside a section. By optimizing this index, it is possible to reveal features such as holes (low density) or grains (high density). The optimization is incorporated into a guided tour so that the search for structure can be dynamic. The approach can be useful for problems when data distributions depart from uniform or normal, as in visually exploring nonlinear manifolds, and functions in multivariate space. Two applications of section pursuit are shown: exploring decision boundaries from classification models, and exploring subspaces induced by complex inequality conditions from a multiple parameter model. The new methods are available in R, in the tourr package. for this article are available online. [ABSTRACT FROM AUTHOR]

Longitudinal (panel) data provide the opportunity to examine temporal patterns of individuals, because measurements are collected on the same person at different, and often irregular, time points. The data is typically visualised using a "spaghetti plot", where a line plot is drawn for each individual. When overlaid in one plot, it can have the appearance of a bowl of spaghetti. With even a small number of subjects, these plots are too overloaded to be read easily. The interesting aspects of individual differences are lost in the noise. Longitudinal data is often modelled with a hierarchical linear model to capture the overall trends, and variation among individuals, while accounting for various levels of dependence. However, these models can be difficult to fit, and can miss unusual individual patterns. Better visual tools can help to diagnose longitudinal models, and better capture the individual experiences. This paper introduces the R package, brolgar (BRowse over Longitudinal data Graphically and Analytically in R), which provides tools to identify and summarise interesting individual patterns in longitudinal data. [ABSTRACT FROM AUTHOR]

In high-dimensional data analysis, the curse of dimensionality reasons that points tend to be far away from the center of the distribution and on the edge of high-dimensional space. Contrary to this, is that projected data tends to clump at the center. This gives a sense that any structure near the center of the projection is obscured, whether this is true or not. A geometric transformation to reverse the curse, is defined in this article, which uses radial transformations on the projected data. It is integrated seamlessly into the grand tour algorithm, and we have called it a burning sage tour, to indicate that it reverses the curse. The work is implemented into the tourr package in R. Several case studies are included that show how the sage visualizations enhance exploratory clustering and classification problems. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

This study was conducted to understand the reasons parents of children with minor conditions attend the Children's Emergency Department (ED), and their views about onsite pediatric same day care (SDC) service as an alternative treatment center. The study was a cross-sectional survey of parents attending an inner-city, district general hospital children's ED, with children aged under 16 years old who were allocated to low triage categories. A convenience sample of 58 parents of 58 children were recruited. All the 58 responses were analyzed. Incomplete questionnaires were not excluded. 47% of attendances were because of minor injury. Most presentations were within 24 hours of the injury or illness. 72% of parents were employed. 91% were registered with a General Practitioner (GP). 29% contacted a GP before the ED visit. The majority of participants who contacted a GP were referred to the ED; others were advised to wait to see if the child's condition improved and to attend the ED if there were any concerns or the child deteriorated in any way. About 50% of those that did not contact GP said the GP surgery was closed and 8% felt the GP could not help. 90% of parents perceived their child's condition as urgent requiring immediate treatment. About 33% of parents said they would be happy for their children to be treated at an onsite SDC center. The study showed limited access to GP services in the community and dissatisfaction with community services and perceived urgency of treatment prompted parents of children with minor conditions to attend the ED. This could mean significant ED attendance by children with minor conditions. The majority of the parents in the study would welcome an onsite pediatric SDC if appropriate to meet their children's care needs. Establishing an onsite SDC may help relieve the ED pressures to attend to more clinically urgent and emergency cases. [ABSTRACT FROM AUTHOR]

A guided tour helps to visualise high-dimensional data by showing low-dimensional projections along a projection pursuit optimisation path. Projection pursuit is a generalisation of principal component analysis in the sense that different indexes are used to define the interestingness of the projected data. While much work has been done in developing new indexes in the literature, less has been done on understanding the optimisation. Index functions can be noisy, might have multiple local maxima as well as an optimal maximum, and are constrained to generate orthonormal projection frames, which complicates the optimization. In addition, projection pursuit is primarily used for exploratory data analysis, and finding the local maxima is also useful. The guided tour is especially useful for exploration because it conducts geodesic interpolation connecting steps in the optimisation and shows how the projected data changes as a maxima is approached. This work provides new visual diagnostics for examining a choice of optimisation procedure based on the provision of a new data object which collects information throughout the optimisation. It has helped to diagnose and fix several problems with projection pursuit guided tour. This work might be useful more broadly for diagnosing optimisers and comparing their performance. The diagnostics are implemented in the R package ferrn. [ABSTRACT FROM AUTHOR]

This article presents a new ensemble learning method for classification problems called projection pursuit random forest (PPF). PPF uses the PPtree algorithm where trees are constructed by splitting on linear combinations of randomly chosen variables. Projection pursuit is used to choose a projection of the variables that best separates the classes. Using linear combinations of variables to separate classes takes the correlation between variables into account which allows PPF to outperform a traditional random forest when separations between groups occurs in combinations of variables. The method presented here can be used in multi-class problems and is implemented into an R package, PPforest, which is available on CRAN. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

Temporal data often has a hierarchical structure, defined by categorical variables describing different levels, such as political regions or sales products. The nesting of categorical variables produces a hierarchical structure. The tsibbletalk package is developed to allow a user to interactively explore temporal data, relative to the nested or crossed structures. It can help to discover differences between category levels, and uncover interesting periodic or aperiodic slices. The package implements a shared tsibble object that allows for linked brushing between coordinated views, and a shiny module that aids in wrapping timelines for seasonal patterns. The tools are demonstrated using two data examples: domestic tourism in Australia and pedestrian traffic in Melbourne. [ABSTRACT FROM AUTHOR]

The increased predictive power of machine learning models comes at the cost of increased complexity and loss of interpretability, particularly in comparison to parametric statistical models. This trade-off has led to the emergence of eXplainable AI (XAI) which provides methods, such as local explanations (LEs) and local variable attributions (LVAs), to shed light on how a model use predictors to arrive at a prediction. These provide a point estimate of the linear variable importance in the vicinity of a single observation. However, LVAs tend not to effectively handle association between predictors. To understand how the interaction between predictors affects the variable importance estimate, we can convert LVAs into linear projections and use the radial tour. This is also useful for learning how a model has made a mistake, or the effect of outliers, or the clustering of observations. The approach is illustrated with examples from categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) response models. The methods are implemented in the R package cheem, available on CRAN. [ABSTRACT FROM AUTHOR]

Kaggle is a data modeling competition service, where participants compete to build a model with lower predictive error than other participants. Several years ago they released a simplified service that is ideal for instructors to run competitions in a classroom setting. This article describes the results of an experiment to determine if participating in a predictive modeling competition enhances learning. The evidence suggests it does. In addition, students were surveyed to examine if the competition improved engagement and interest in the class. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. Most indexes have been developed to detect departure from known distributions, such as normality, or to find separations between known groups. Here, we are interested in finding projections revealing potentially complex bivariate patterns, using new indexes constructed from scagnostics and a maximum information coefficient, with a purpose to detect unusual relationships between model parameters describing physics phenomena. The performance of these indexes is examined with respect to ideal behaviour, using simulated data, and then applied to problems from gravitational wave astronomy. The implementation builds upon the projection pursuit tools available in the R package, tourr, with indexes constructed from code in the R packages, binostics, minerva and mbgraphic. [ABSTRACT FROM AUTHOR]

Taking projections of high-dimensional data is a common analytical and visualization technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualizing data with concavities, or nonlinear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots in interactive data visualization. This short technical note describes a simple approach for slicing in the orthogonal space of projections obtained when running a tour, thus presenting the viewer with an interpolated sequence of sliced projections. The method has been implemented in R as an extension to the tourr package, and can be used to explore for concave and nonlinear structures in multivariate distributions. for this article are available online. [ABSTRACT FROM AUTHOR]

Calendars are broadly used in society to display temporal information and events. This article describes a new calendar display for plotting data that include a layout algorithm with many options, and faceting functionality. The functions use modular arithmetic on the date variable to restructure the data into a calendar format. The user can apply the grammar of graphics to create plots inside each calendar cell, and thus the displays synchronize neatly with ggplot2 graphics. The motivating application is studying pedestrian behavior in Melbourne, Australia, based on counts which are captured at hourly intervals by sensors scattered around the city. Faceting by the usual features such as day and month is insufficient to examine the behavior. Making displays on a monthly calendar format helps to understand pedestrian patterns relative to events such as work days, weekends, holidays, and special events. The functions for the calendar algorithm are available in the R package sugrrants. for this article are available online. [ABSTRACT FROM AUTHOR]

Mining temporal data for information is often inhibited by a multitude of formats: regular or irregular time intervals, point events that need aggregating, multiple observational units or repeated measurements on multiple individuals, and heterogeneous data types. This work presents a cohesive and conceptual framework for organizing and manipulating temporal data, which in turn flows into visualization, modeling, and forecasting routines. Tidy data principles are extended to temporal data by: (1) mapping the semantics of a dataset into its physical layout; (2) including an explicitly declared "index" variable representing time; (3) incorporating a "key" comprising single or multiple variables to uniquely identify units over time. This tidy data representation most naturally supports thinking of operations on the data as building blocks, forming part of a "data pipeline" in time-based contexts. A sound data pipeline facilitates a fluent workflow for analyzing temporal data. The infrastructure of tidy temporal data has been implemented in the R package, called tsibble. for this article are available online. [ABSTRACT FROM AUTHOR]

Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks. Author summary: Biological disciplines face the challenge of increasingly large and complex data. One necessary approach toward eliciting information is data visualization. Newer visualization tools incorporate interactive capabilities that allow scientists to extract information more efficiently than static counterparts. In this paper, we introduce technology that allows multiple independent layers of interactive visualization written in open-source code. This technology can be repurposed across various biological problems. Here, we apply this technology to RNA-sequencing data, a popular next-generation sequencing approach that provides snapshots of RNA quantity in biological samples at given moments in time. It can be used to investigate cellular differences between health and disease, cellular changes in response to external stimuli, and additional biological inquiries. RNA-sequencing data is large, noisy, and biased. It requires sophisticated normalization. The most popular open-source RNA-sequencing data analysis software focuses on models, with little emphasis on integrating effective visualization tools. This is despite sound evidence that RNA-sequencing data is most effectively explored using graphical and numerical approaches in a complementary fashion. The software we introduce can make it easier for researchers to use models and visuals in an integrated fashion during RNA-sequencing data analysis. [ABSTRACT FROM AUTHOR]

Summary: We examine the relationships between electoral socio‐demographic characteristics and two‐party preferences in the six Australian federal elections held between 2001 and 2016. Socio‐demographic information is derived from the Australian Census which occurs every 5 years. Since a census is not directly available for each election, an imputation method is employed to estimate census data for the electorates at the time of each election. This accounts for both spatial and temporal changes in electoral characteristics between censuses. To capture any spatial heterogeneity, a spatial error model is estimated for each election, which incorporates a spatially structured random effect vector. Over time, the impact of most socio‐demographic characteristics that affect electoral two‐party preference do not vary, with age distribution, industry of work, incomes, household mobility and relationships having strong effects in each of the six elections. Education and unemployment are among those that have varying effects. All data featured in this study have been contributed to the eechidnaR package (available on CRAN). This paper models all Australian federal elections from 2001‐2016 to examine the socio‐demographic drivers of electorates' two‐party preferred vote. [ABSTRACT FROM AUTHOR]

Dynamic low-dimensional linear projections of multivariate data collectively known as tours provide an important tool for exploring multivariate data and models. The R package tourr provides functions for several types of tours: grand, guided, little, local and frozen. Each of these can be viewed dynamically, or saved into a data object for animation. This paper describes a new package, spinifex, which provides a manual tour of multivariate data where the projection coefficient of a single variable is controlled. The variable is rotated fully into the projection, or completely out of the projection. The resulting sequence of projections can be displayed as an animation, with functions from either the plotly or gganimate packages. By varying the coefficient of a single variable, it is possible to explore the sensitivity of structure in the projection to that variable. This is particularly useful when used with a projection pursuit guided tour to simplify and understand the solution. The use of the manual tour is applied particle physics data to illustrate the sensitivity of structure in a projection to specific variable contributions. [ABSTRACT FROM AUTHOR]

Graphics play a crucial role in statistical analysis and data mining. Being able to quantify structure in data that is visible in plots, and how people read the structure from plots is an ongoing challenge. The lineup protocol provides a formal framework for data plots, making inference possible. The data plot is treated like a test statistic, and lineup protocol acts like a comparison with the sampling distribution of the nulls. This article describes metrics for describing structure in data plots and evaluates them in relation to the choices that human readers made during several large Amazon Turk studies using lineups. The metrics that were more specific to the plot types tended to better match subject choices, than generic metrics. The process that we followed to evaluate metrics will be useful for general development of numerically measuring structure in plots, and also in future experiments on lineups for choosing blocks of pictures. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This article describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble since it is produced by bagging multiple trees. The process of bagging and combining results from multiple trees produces numerous diagnostics which, with interactive graphics, can provide a lot of insight into class structure in high dimensions. Various aspects of models are explored in this article, to assess model complexity, individual model contributions, variable importance and dimension reduction, and uncertainty in prediction associated with individual observations. The ideas are applied to the random forest algorithm and projection pursuit forest but could be more broadly applied to other bagged ensembles helping in the interpretability deficit of these methods. Interactive graphics are built in R using the ggplot2, plotly, and shiny packages. [ABSTRACT FROM AUTHOR]

Temporal data are information measured in the context of time. This contextual structure provides components that need to be explored to understand the data and that can form the basis of interactions applied to the plots. In multivariate time series, we expect to see temporal dependence, long term and seasonal trends, and cross-correlations. In longitudinal data, we also expect within and between subject dependence. Time series and longitudinal data, although analyzed differently, are often plotted using similar displays. We provide a taxonomy of interactions on plots that can enable exploring temporal components of these data types, and describe how to build these interactions using data transformations. Because temporal data are often accompanied other types of data we also describe how to link the temporal plots with other displays of data. The ideas are conceptualized into a data pipeline for temporal data and implemented into the R packagecranvas. This package provides many different types of interactive graphics that can be used together to explore data or diagnose a model fit. [ABSTRACT FROM AUTHOR]

Visualization can help in model building, diagnosis, and in developing an understanding about how a model summarizes data. This paper proposes three strategies for visualizing statistical models: (i) display the model in the data space, (ii) look at all members of a collection, and (iii) explore the process of model fitting, not just the end result. Each strategy is accompanied by examples, including manova, classification algorithms, hierarchical clustering, ensembles of linear models, projection pursuit, self-organizing maps, and neural networks. [ABSTRACT FROM AUTHOR]

This article describes new user controls for examining high-dimensional data using low-dimensional linear projections and slices. A user can interactively change the contribution of a given variable to a low-dimensional projection, which is useful for exploring the sensitivity of structure to particular variables. The user can also interactively shift the center of a slice, for example, to explore how structure changes in local subspaces. The Mathematica package as well as example notebooks are provided, which contain functions enabling the user to experiment with these new manual controls, with one specifically for exploring regions and boundaries produced by classification models. The advantage of Mathematica is its linear algebra capabilities and interactive cursor location controls. Some limited implementation has also been made available in the R package tourr. for this article are available online. [ABSTRACT FROM AUTHOR]

1. The megadiverse herbivores and their host plants are a major component of biodiversity, and their interactions have been hypothesised to drive the diversification of both. 2. If plant diversity influences the diversity of insects, there is an expectation that insect species richness will be strongly correlated with host-plant species richness. This should be observable at two levels (i) more diverse host-plant groups should harbour more species of insects, and (ii) the species richness of a group of insects should correlate with the richness of the host groups it uses. However, such a correlation is also consistent with a hypothesis of random host use, in which insects encounter and use hosts in proportion to the diversity of host plants. Neither of these expectations has been widely tested. 3. These expectations were tested using data from a species-rich group of insects - the Coccidae ( Hemiptera). 4. Significant positive correlations were found between the species richness of coccid clades (genera) and the species richness of the host-plant family or families upon which the clades occur. On a global scale, more closely related plant families have more similar communities of coccid genera but the correlation is weak. 5. Random host use could not be rejected for many coccids but randomisation tests and similarity of coccid communities on closely related plant families show that there is non-random host use in some taxa. Overall, our results support the idea that plant diversity is a driver of species richness of herbivorous insects, probably via escape-and-radiate or oscillation-type processes. [ABSTRACT FROM AUTHOR]

This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package. [ABSTRACT FROM AUTHOR]

Graphical statistics plays a very important role in research and industry. As a statistician, it is very useful to know how analysts make decisions based on plots, so that the plots can be improved for better performance. With the help of eye-tracking equipment, researchers can assess this. This paper describes the use of an eye-tracker for assessing how people look at lineup plots that are used in visual inference. The experiment included a comparison of several plot types, histogram, density, scatterplot and dotplot for two group data, and the use of side-by-side boxplots and scatterplots for assessing the importance of predictors in a regression model. Results are compared with those from a larger visual inference study using Amazon's Mechanical Turk. The eye-tracker can be manageably used only on a small sample of people, to give detailed information plot about reading behavior, so it is ideally used prior to a large human subjects study to help refine the experimental procedures, or as a post-processing step to better understand any surprises in the results. [ABSTRACT FROM AUTHOR]

The author respond to the comments on their article about the future of visualization within the field of statistics. Topics discussed include challenges in visualizing the model in the data space, data analysis and modeling, and the interplay between modelling and visualization. Also they mention the application of organization algorithm to the dendrogram.

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data. This article introduces new indices derived from linear discriminant analysis that can be used for exploratory supervised classification. [ABSTRACT FROM AUTHOR]

This article presents aspects of the implementation of a bidirectional link between the Geographic Information System (GIS) ArcView[TM] and the interactive dynamic statistical graphics program XGobi. We describe the main functionality of the link, the underlying remote procedure call (RPC) mechanism, and internal data structures, and discuss topics such as security, concurrency, and linked brushing. We think that these topics are of particular interest to software authors intending to link similar software packages, and software users learning about strengths (and weaknesses) of the implementation of our link. [ABSTRACT FROM AUTHOR]

Discusses how to manually control visualization methods for high-dimensional projections. Functionality for controlling one and two-dimensional projections; Constrained manipulations; Applications of interactive manipulation tools to projection pursuit.

Proposes a rudimentary taxonomy of interactive data visualization based on finding Gestalt, posing queries and making comparisons. Interactive view manipulations; XGobi system for multivariate data visualization; Powers and limitations of particular focusing, linking and arranging tools; Illustration of asymptotic use of high-dimensional cubes for visualizing factorial experiments.

Discusses how two graphical methods for exploring high dimensional data--the `grand tour' dynamic model and the `projection pursuit' static model--complement each other for exploratory data analysis. Optimization adaptation of tour meovement; Monitoring window; Bitmap interface; Navigational tools; Index choices; Impact of sphering; Inclusion of user-defined index functions; Linked tours.

The article presents an overview of articles on the oil spill at the Deepwater Horizon rig by the oil firm BP PLC, which have been presented during a Data Exposition held by the American Statistical Association in 2011. It mentions the paper "Effect of Oil Spill on Birds: A Graphical Assay of the Deepwater Horizon Oil Spill's Impact on Birds" by T. Tran et al. and the paper "A Graphical Exploration of the Deepwater Horizon Oil Spill" by Lendie Follett et al.

With the international R user conference, useR! 2011, approaching, many participants may be contemplating how to put their thoughts together for presentation. This paper provides some suggestions for giving presentations and making posters. [ABSTRACT FROM AUTHOR]

Deconstructing a time index into time granularities can assist in exploration and automated analysis of large temporal datasets. This article describes classes of time deconstructions using linear and cyclic time granularities. Linear granularities respect the linear progression of time such as hours, days, weeks and months. Cyclic granularities can be circular such as hour-of-the-day, quasi-circular such as day-of-the-month, and aperiodic such as public holidays. The hierarchical structure of granularities creates a nested ordering: hour-of-the-day and second-of-the-minute are single-order-up. Hour-of-the-week is multiple-order-up, because it passes over day-of-the-week. Methods are provided for creating all possible granularities for a time index. A recommendation algorithm provides an indication whether a pair of granularities can be meaningfully examined together (a "harmony"), or when they cannot (a "clash"). Time granularities can be used to create data visualizations to explore for periodicities, associations and anomalies. The granularities form categorical variables (ordered or unordered) which induce groupings of the observations. Assuming a numeric response variable, the resulting graphics are then displays of distributions compared across combinations of categorical variables. The methods implemented in the open source R package gravitas are consistent with a tidy workflow, with probability distributions examined using the range of graphics available in ggplot2. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

Statistical inference provides the protocols for conducting rigorous science, but data plots provide the opportunity to discover the unexpected. These disparate endeavours are bridged by visual inference, where a lineup protocol can be employed for statistical testing. Human observers are needed to assess the lineups, typically using a crowd‐sourcing service. This paper describes a new approach for computing statistical significance associated with the results from applying a lineup protocol. It utilizes a Dirichlet distribution to accommodate different levels of visual interest in individual null panels. The suggested procedures facilitate statistical inference for a broader range of data problems. [ABSTRACT FROM AUTHOR]

AbstractThe usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure. However, what if the explanation provided by this model is completely different from another model and different again from another model despite all having similarly good fit statistics? Is it possible that the equally effective models put the spotlight on different relationships in the data? Inspired by Anscombe’s quartet, this article introduces a Rashomon Quartet, that is a set of four models built on a synthetic dataset which have practically identical predictive performance. However, the visual exploration reveals distinct explanations of the relations in the data. This illustrative example aims to encourage the use of methods for model visualization to compare predictive models beyond their performance. [ABSTRACT FROM AUTHOR]

A recent paper on visualizing the sensitivity of hadronic experiments to nucleon structure (Wang et al. in arXiv:1803.02777, 2018) introduces the tool PDFSense which defines measures to allow the user to judge the sensitivity of PDF fits to a given experiment. The sensitivity is characterized by high-dimensional data residuals that are visualized in a 3-d subspace of the 10 first principal components or using non-linear embeddings. We show how a tour, a dynamic visualisation of high dimensional data, can extend this tool beyond 3-d relationships. This approach enables resolving structure orthogonal to the 2-d viewing plane used so far, and hence finer tuned assessment of the sensitivity. [ABSTRACT FROM AUTHOR]

Statistical inference provides the protocols for conducting rigorous science, but data plots provide the opportunity to discover the unexpected. These disparate endeavours are bridged by visual inference, where a lineup protocol can be employed for statistical testing. Human observers are needed to assess the lineups, typically using a crowd-sourcing service. This paper describes a new approach for computing statistical significance associated with the results from applying a lineup protocol. It utilizes a Dirichlet distribution to accommodate different levels of visual interest in individual null panels. The suggested procedures facilitate statistical inference for a broader range of data problems. [ABSTRACT FROM AUTHOR]

Since 1983, the Sections on Statistical Computing and Statistical Graphics of the American Statistical Association (ASA) have held a Data Exposition competition (usually called "Data Expo") as part of the Joint Statistical Meetings (JSM). In 2013, the focus was on data from the Knight Foundation (http://www.knightfoundation.org/), using data collected by Gallup that examined the emotional attachment of people to their community. Are there specific community attributes that attract people to certain communities, tempt them to set down roots and commit to the community for the long term? McNamara ([7]) focused on the idea of community meta-knowledge, which is essentially majority group empathy or understanding of how minorities experience their community. [Extracted from the article]

The tourr package in R has several algorithms and displays for showing multivariate data as a sequence of low-dimensional projections. It can display as a movie but has no capacity for interaction, such as stop/go, change tour type, drop/add variables. The tourrGui package provides these sorts of controls, but the interface is programmed with the dated RGtk2 package. This work explores using custom messages to pass data from R to D3 for viewing, using the Shiny framework. This is an approach that can be generally used for creating all sorts of interactive graphics. [ABSTRACT FROM AUTHOR]

The author presents his view on Leland Wilkinson's article "The Future of Statistical Computing" and discusses the impact of computing on statistics and vice versa. He states that statistics is a foundation for many areas of computing, and driven by these needs, much better statistical software are needed. He relates that robust libraries such as those developed by the numerical community needed in the future and statistics will be used as a service, not as a tool for experts. He mentions that the disseminated adoption of statistical computing platforms are going to alter the role of the statistician.

The complexity of linear mixed-effects (LME) models means that traditional diagnostics are rendered less effective. This is due to a breakdown of asymptotic results, boundary issues, and visible patterns in residual plots that are introduced by the model fitting process. Some of these issues are well known and adjustments have been proposed. Working with LME models typically requires that the analyst keeps track of all the special circumstances that may arise. In this article, we illustrate a simpler but generally applicable approach to diagnosing LME models. We explain how to use new visual inference methods for these purposes. The approach provides a unified framework for diagnosing LME fits and for model selection. We illustrate the use of this approach on several commonly available datasets. A large-scale Amazon Turk study was used to validate the methods. R code is provided for the analyses. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

Aim: To test whether novel and previously hypothesized biogeogaphic barriers in the Australian Tropics represent significant disjunction points or hard barriers, or both, to the distribution of plants. Location: Australian tropics: Australian Monsoon Tropics and Australian Wet Tropics. Methods: The presence or absence of 6,861 plant species was scored across 13 putative biogeographic barriers in the Australian Tropics, including two that have not previously been recognised. Randomizations of these data were used to test whether more species showed disjunctions (gaps in distribution) or likely barriers (range limits) at these points than expected by chance. Results: Two novel disjunctions in the Australian Tropics flora are identified in addition to eleven putative barriers previously recognized for animals. Of these, eleven disjunction points (all within the Australian Monsoon Tropics) were found to correspond to range-ending barriers to a significant number of species, while neither of the two disjunctions found within the Australian Wet Tropics limited a significant number of species’ ranges. Main conclusions: Biogeographic barriers present significant distributional limits to native plant species in the Australian Monsoon Tropics but not in the Australian Wet Tropics. [ABSTRACT FROM AUTHOR]