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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]

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]

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]

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]

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.

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]

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]

Ecological fingerprints of climate change are becoming increasingly evident at broad geographical scales as measured by species range shifts and changes in phenology. However, finer-scale species-level responses to environmental fluctuations may also provide an important bellwether of impending future community responses. Here we examined changes in abundance of butterfly species along a hydrological gradient of six montane meadow habitat types in response to drought. Our data collection began prior to the drought, and we were able to track changes for 11 years, of which eight were considered mild to extreme drought conditions. We separated the species into those that had an affinity for hydric vs. xeric habitats. We suspected that drought would favor species with xeric habitat affinities, but that there could be variations in species-level responses along the hydrological gradient. We also suspected that mesic meadows would be most sensitive to drought conditions. Temporal trajectories were modeled for both species groups (hydric vs. xeric affinity) and individual species. Abundances of species with affinity for xeric habitats increased in virtually all meadow types. Conversely, abundances of species with affinity for hydric habitats decreased, particularly in mesic and xeric meadows. Mesic meadows showed the most striking temporal abundance trajectory: Increasing abundances of species with xeric habitat affinity were offset by decreasing or stable abundances of species with hydric habitat affinity. The one counterintuitive finding was that, in some hydric meadows, species with affinity for hydric habitats increased. In these cases, we suspect that decreasing moisture conditions in hydric meadows actually increased habitat suitability because sites near the limit of moisture extremes for some species became more acceptable. Thus, species responses were relatively predictable based upon habitat affinity and habitat location along the hydrological gradient, and mesic meadows showed the highest potential for changes in community composition. The implications of these results are that longer-term changes due to drought could simplify community composition, resulting in prevalence of species tolerant to drying conditions and a loss of species associated with wetter conditions. We contend that this application of gradient analysis could be valuable in assessing species vulnerability of other taxa and ecosystems. [ABSTRACT FROM AUTHOR]

In soybean [ Glycine max ( L.) Merr.], iron deficiency results in interveinal chlorosis and decreased photosynthetic capacity, leading to stunting and yield loss. In this study, gene expression analyses investigated the role of soybean replication protein A ( RPA) subunits during iron stress. Nine RPA homologs were significantly differentially expressed in response to iron stress in the near isogenic lines ( NILs) Clark (iron efficient) and Isoclark (iron inefficient). RPA homologs exhibited opposing expression patterns in the two NILs, with RPA expression significantly repressed during iron deficiency in Clark but induced in Isoclark. We used virus induced gene silencing ( VIGS) to repress GmRPA3 expression in the iron inefficient line Isoclark and mirror expression in Clark. GmRPA3-silenced plants had improved IDC symptoms and chlorophyll content under iron deficient conditions and also displayed stunted growth regardless of iron availability. RNA- Seq comparing gene expression between GmRPA3-silenced and empty vector plants revealed massive transcriptional reprogramming with differential expression of genes associated with defense, immunity, aging, death, protein modification, protein synthesis, photosynthesis and iron uptake and transport genes. Our findings suggest the iron efficient genotype Clark is able to induce energy controlling pathways, possibly regulated by SnRK1/ TOR, to promote nutrient recycling and stress responses in iron deficient conditions. [ABSTRACT FROM AUTHOR]