![]() While this method appears to be effective under most circumstances, it is also subject to some limitations. A more recent iteration of this technique described by Deochand ( 2017) also includes the steps for using data labels as phase change labels. However, this method also has limitations, as it only allows for solid phase change lines and has difficulty maintaining its appearance when saved as a portable document file (PDF).Ī third recommendation involves using scatterplots and error bars to generate a phase change line (Vanselow & Bourrett, 2012). This means that the lines move and resize with the graph and allow for the easy addition of new lines for future data. The primary benefit of this method is the inclusion of phase change lines that are part of the graph. Furthermore, unless these objects are pasted within the graph, they do not carry over when the graph is copied and pasted into another program such as Microsoft Word® or PowerPoint®.Ī more recent recommendation to address this issue involves converting a separate data series on a line graph into a column and modifying its gradient and transparency properties to create a phase change line (Dubuque, 2015). However, this solution has been limited as these objects are external to the graph and therefore do not re-adjust their position and size when new data is added or the height and width of the graph is updated. The earliest solution to phase change lines and labels involved the use of drawing tools and text boxes (Carr & Burkholder, 1998 Dixon et al., 2009 Pritchard, 2009). Over the years, a few solutions have been developed to address these issues. ![]() Phase change lines and their corresponding labels are two areas in particular that are not intuitive to Excel® (Dubuque, 2015). However, this software has several limitations that make it difficult for behavior analysts to meet common graphing conventions used in the field. Then, since the priori distribution is known, I can use the Distributions package to produce the density – where I do have some control of the scaling.Many behavior analysts rely on Microsoft Excel® for graphing and interpreting clinical data. ![]() I can plot the posterior plot using density, then use your trick of reading ylims.Of course – if I want to compare a posterior plot with a priori plot, I can get around this… In summary, if I want to put two density plots in the same plot, I would like to scale the y value of each plot so that their peak values are comparable. I assume some histogram function is used (but what bin size?), and then a smooth function is fitted to the histogram … I would guess. Using density plot, the plotting algorithm produces the series to plot ( x values, y values) based on “random” outcomes (e.g., from Turing) – but the user doesn’t see the y values. For such a comparison, I don’t really care about the scaling in the ordinate direction – I mainly care about how one distribution is shifted and made wider/narrower compared to the other in the abscissa direction… Suppose I have two density plots with wildly different maximal ylims values, and I want to show them in the same plot (i.e., comparing apriori and aposteriori distributions).
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