9/12/2023 0 Comments Jmp graph builder downloadContours in 2D Scatterplots for two continuous variables have some of the same disadvantages as scatterplots in 1D. Compared to the Violin representation, the mode lines and outliers give a more quantitative presentation of the underlying density function. When the underlying data is not unimodal, this can give a better picture of the underlying distribution of the data. The magnitude of the curves is determined by a scaling option, with options for Equal Area, Equal Width, or Weighted Area.ĥ One distinction between box plots and HDR plots is that the 50% and 99% probability regions in an HDR plot are not necessarily continuous. The default view for 1D contours is the Violin plot, which plots the density function with symmetry about the 1D axis. The Graph Builder Contour element uses a probability density function for a given kernel bandwidth to illustrate the shape of a 1D point distribution. There are several variations on how to draw the whiskers in this example they extend to the outermost point that lies within 1.5x the interquartile range from the box. A line is drawn at the median, giving additional detail. The box extends from the first quartile to the third quartile, so it contains 50% of the expected observations. There are several variations on the visual representation for a boxplot, but they are all based on quartiles. The boxplot will be useful for comparison with the new contour techniques. One widely used technique for summarizing 1D data is the boxplot, shown here. Some observations can be made from the scatterplots, such as that the outliers in the survivor group that paid roughly twice what the others (in either group) paid.Ģ To compare these two groups further, some additional analysis is necessary to characterize the groups. In this example using the Titanic sample data in JMP, the fares paid by the survivors of the tragedy are compared to the fares paid by those that perished. When the points are very dense, it becomes difficult to characterize the distribution of the points, and especially to make comparisons between multiple groups. Contours in 1D The scatterplot is a direct way to visualize continuous data and is often the default way to get a visual representation of continuous data. The strengths and weaknesses of each contour visualization techniques will be discussed, as well as the various options that are supported for the different types, including smoothing parameters, outliers, and alpha-shapes for non-convex domains. This paper will concentrate on the six different forms of contours that are available in Graph Builder, including two types of 1D density contours, three types of 2D density contours, and triangulations for 2D value contours. In JMP, contours are used in several platforms, including Bivariate, Contour Plot, Contour Profiler, Graph Builder, and others. Domainspecific terminology for contours is often based on the underlying function isobars, isotherms, isopleths, and many more. Contours in 2D are curves along which a continuous function has a constant value. Schikore Principal Software Developer, JMP Introduction Contour plots are a common visualization technique for summarizing the shape of a dense collection of data. 1 Graph Builder Contour Plots in JMP US-30MP-247 Daniel R.
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