Graphical Exploratory Data Analysis

Toit, S. H. C. du; Steyn, A. G. W.; Stumpf, R. H.

doi:10.1007/978-1-4612-4950-4

Cited by 124 publications

(68 citation statements)

References 2 publications

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“…The number of cluster was determined using Toit et al (1986) The experiment was laid out in a randomised complete block design ( RCBD) with three replications in a single row. The size of each block, row, and spaces between rows and between blocks were 2.00m x 22.40m, 0.20m x 2.00m, 0.20m and 2.00m, respectively.…”

Section: Methodsmentioning

confidence: 99%

Variation and association analyses on morphological characters of linseed (<I>Linum usitatissimum</I> L.) in Ethiopia

Negash

Asfaw

Yibrah

2006

SEJS

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Morphological studies of linseed (Linum usitatissimum L.) of Ethiopia were undertaken in this study on 56 accessions collected from 10 administrative regions (former regions) classified into seven altitude classes. The seeds were planted during 2001 growing season in Bale-Goro "wereda" using randomized complete block design (RCBD) on three replications with the aim of investigating the morphological diversity in the accessions. Over the entire accession, except seed number per boll, all the characters showed significant variation. However, regional groups of linseed accessions showed insignificant variations only for secondary branches number per plant (SBP), days to flowering (DTF) and days to maturity (DTM) among themselves, and altitudinal groups of linseed accessions only for seed number per boll (SNB) and days to flowering (DTF). The analysis for coefficient of variation and cluster analysis showed that accessions from Gojam and Tigray were less diversified. About 53% of the total associations showed significant correlation and out of this 71% of the correlations were with the positive significant correlation coefficients. Cluster analysis indicates that accessions collected from the two extremes of altitude classes have variations for the studied traits. It is only Tigray Region that did not contribute member accession to cluster III, whereas Gondar and Bale regions contribute member accessions to 80% of clusters. Factor analysis showed that of the total variation (73.86%) 30.04% and 27.29% were accounted for by first and second principal components, respectively. The highest Shannon diversity index (0.322±0.039) was recorded for boll size and Shannon diversity index for overall pooled mean was 0.223±0.063.

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Section: Methodsmentioning

confidence: 99%

Variation and association analyses on morphological characters of linseed (<I>Linum usitatissimum</I> L.) in Ethiopia

Negash

Asfaw

Yibrah

2006

SEJS

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“…We will use glyphs that have traditionally been used to visualize data. Various glyphs have been proposed in the literature, among them profile glyphs (Du Toit et al, 1986), Chernoff faces (Chernoff, 1973) and stars (Anderson, 1957, Siegel et al, 1972, Gnanadesikan, 1977. We will focus on star plots, but instead of using them to visualize data, they are used to visualize parameters.…”

Section: Glyphs For the Visualization Of Parametersmentioning

confidence: 99%

Visualization of Categorical Response Models: From Data Glyphs to Parameter Glyphs

Tutz

Schauberger

2012

Journal of Computational and Graphical Statistics

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The multinomial logit model is the most widely used model for nominal multi-category responses. One problem with the model is that many parameters are involved, another that interpretation of parameters is much harder than for linear models because the model is non-linear. Both problems can profit from graphical representations. We propose to visualize the effect strengths by star plots, where one star collects all the parameters connected to one term in the linear predictor. In simple models one star refers to one explanatory variable. In contrast to conventional star plots, which are used to represent data, the plots represent parameters and are considered as parameter glyphs. The set of stars for a fitted model makes the main features of the effects of explanatory variables on the response variable easily accessible. The method is extended to ordinal models and illustrated by several data sets.

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“…On the perceptual front, Chernoff (1973, p. 366) has acknowledged that some display features are difficult to detect in some face visualizations, and it is also not possible to omit facial features to indicate the absence of a property. On the cognitive front, it seems likely that the problem of assigning underlying variables to perceptual features (Everitt, 1978;Toit et al, 1986;Manly, 1994) is more severe in the case of faces, because of their inherent meaning and different saliencies. There is also a possibility of individual differences having an impact in the semantic perception of facial features (Chatfield and Collins, 1980).…”

Section: Article In Pressmentioning

confidence: 99%

Visualizations of binary data: A comparative evaluation

Lee

Butavicius

Reilly

2003

International Journal of Human-Computer Studies

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Data visualization has the potential to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using nongraphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, information retrieval, and intelligence analysis. Unfortunately, while various different approaches are available for data visualization, there have been few rigorous evaluations of their effectiveness. This paper presents the results of three controlled experiments comparing the ability of four different visualization approaches to help people answer meaningful questions for binary data sets. Two of these visualizations, Chernoff faces and star glyphs, represent objects using simple icon-like displays. The other two visualizations use a spatial arrangement of the objects, based on a model of human mental representation, where more similar objects are placed nearer each other. One of these spatial displays uses a common features model of similarity, while the other uses a distinctive features model. The first experiment finds that both glyph visualizations lead to slow, inaccurate answers being given with low confidence, while the faster and more confident answers for spatial visualizations are only accurate when the common features similarity model is used. The second experiment, which considers only the spatial visualizations, supports this finding, with the common features approach again producing more accurate answers. The third experiment measures human performance using the raw data in tabular form, and so allows the usefulness of visualizations in facilitating human performance to be assessed. This experiment confirms that people are faster, more confident and more accurate when an appropriate visualization of the data is made available. r

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Graphical Exploratory Data Analysis

Abstract: The use of general descriptive names, trade names, trademarks, etc. in this publication, even if the former are not especially identified, is not to be taken as a sign that such names, as understood by the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone.

Cited by 124 publications

References 2 publications

Variation and association analyses on morphological characters of linseed (<I>Linum usitatissimum</I> L.) in Ethiopia

Variation and association analyses on morphological characters of linseed (<I>Linum usitatissimum</I> L.) in Ethiopia

Visualization of Categorical Response Models: From Data Glyphs to Parameter Glyphs

Visualizations of binary data: A comparative evaluation

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