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A Poincaré plot, named after Henri Poincaré, is a graphical representation used to visualize the relationship between consecutive data points in time series to detect patterns and irregularities in the time series, revealing information about the stability of dynamical systems, providing insights into periodic orbits, chaotic motions, and bifurcations.
By depicting experimental results, the Davenport diagram provides a simple approach to describing the behavior of this complex system. Figure 2 shows a Davenport diagram as commonly depicted in textbooks and the literature. To understand how the diagram is to be interpreted, it is helpful to understand how the diagram is derived in the first place.
The idea is that the first clusters will add much information (explain a lot of variation), since the data actually consist of that many groups (so these clusters are necessary), but once the number of clusters exceeds the actual number of groups in the data, the added information will drop sharply, because it is just subdividing the actual groups.
A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results. [1] It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials .
This results in data points with low p values (highly significant) appearing toward the top of the plot. The x axis is the logarithm of the fold change between the two conditions. The logarithm of the fold change is used so that changes in both directions appear equidistant from the center.
The radar chart is a chart and/or plot that consists of a sequence of equi-angular spokes, called radii, with each spoke representing one of the variables. The data length of a spoke is proportional to the magnitude of the variable for the data point relative to the maximum magnitude of the variable across all data points.
Since funnel plots are principally visual aids for detecting asymmetry along the treatment effect axis, this makes them considerably easier to interpret. Criticism [ edit ]
An example of waterfall charts. Here, there are 3 total columns called Main Column1, Middle Column, and End Value. The accumulation of successive two intermediate columns from the first total column (Main Column1) as the initial value results in the 2nd total column (Middle Column), and the rest accumulation results in the last total column (End Value) as the final value.