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A myriad of design rules for what constitutes a “good” colormap can be found in the literature. Some common rules include order, uniformity, and high discriminative power. However, the meaning of many of these terms is often ambiguous or open to interpretation. At times, different authors may use the same term to describe different concepts or the same rule is described by varying nomenclature. These ambiguities stand in the way of collaborative work, the design of experiments to assess the characteristics of colormaps, and automated colormap generation. In this paper, we review current and historical guidelines for colormap design. We propose a specified taxonomy and provide unambiguous mathematical definitions for the most common design rules. [ABSTRACT FROM PUBLISHER]

Purpose: The short form six dimensions (SF-6D) are derived from the SF-36 Health Survey with the intention that item data of the latter are often converted to a preference value, which was subsequently used in economic evaluations of interventions. The aim was to compare the equivalence and sensitivity of health preference values derived from the SF-36/SF-12 Health Surveys to that measured directly by the SF-6D for chronic hepatitis B (CHB) patients. Methods: This was a secondary analysis of the SF-6D and SF-36 data from a baseline sample of 589 patients with CHB infection with different stages of liver diseases. Degree of agreement (equivalence) between direct-measured and derived SF-6D values was determined using spearman correlation and intra-class correlation. Sensitivity and discriminative power of different SF-6D values were compared by standardized effect size and relative efficiency (RE) statistics. Results: Significant differences in the direct-measured or derived SF-6D preference values were found between CHB groups. Degree of agreement between SF-6D values was satisfactory. Direct-measured SF-6D was the most efficient, followed by SF-12-derived and the SF-36-derived was the least, based on the standardized effect size and the RE statistics. Sensitivity and discriminative power of direct-measured SF-6D were superior to derived SF-6D among people with different CHB health states. Conclusions: Although direct-measured and derived SF-6D preference values had satisfactory sensitivity in discriminating between CHB groups, direct-measured SF-6D is the most sensitive and preferable method of obtaining health preference. [ABSTRACT FROM AUTHOR]

Several object-oriented cohesion metrics have been proposed in the literature. These metrics aim to measure the relationship between class members, namely, methods and attributes. Different metrics use different models to represent the connectivity pattern of cohesive interactions (CPCI) between class members. Most of these metrics are normalized to allow for easy comparison of the cohesion of different classes. However, in some cases, these metrics obtain the same cohesion values for different classes that have the same number of methods and attributes but different CPCIs. This leads to incorrectly considering the classes to be the same in terms of cohesion, even though their CPCIs clearly indicate that the degrees of cohesion are different. We refer to this as a lack of discrimination anomaly (LDA) problem. In this paper, we list and discuss cases in which the LDA problem exists, as expressed through the use of 16 cohesion metrics. In addition, we empirically study the frequent occurrence of the LDA problem when the considered metrics are applied to classes in five open source Java systems. Finally, we propose a metric and a simulation-based methodology to measure the discriminative power of cohesion metrics. The discrimination metric measures the probability that a cohesion metric will produce distinct cohesion values for classes with the same number of attributes and methods but different CPCIs. A highly discriminating cohesion metric is more desirable because it exhibits a lower chance of incorrectly considering classes to be cohesively equal when they have different CPCIs. [ABSTRACT FROM AUTHOR]