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The top row is a series of plots using the escape time algorithm for 10000, 1000 and 100 maximum iterations per pixel respectively. The bottom row uses the same maximum iteration values but utilizes the histogram coloring method. Notice how little the coloring changes per different maximum iteration counts for the histogram coloring method plots.
The data of the liquids given from the table above is then graphed on the Zisman Plot (Figure 2) with the independent variable as the surface tension of the liquid in dynes/cm and the dependent variable as 1-cos(θ SL). There also are different variations of the Zisman plot since the Y-axis is unitless as seen in Table 1 and as mentioned above.
To this plot is added a line at the average value, x and lines at the UCL and LCL values. On a separate graph, the calculated ranges MR i are plotted. A line is added for the average value, MR and second line is plotted for the range upper control limit (UCL r).
This is a technique used as a stability criterion in the field of classical control theory developed by Walter R. Evans which can determine stability of the system. The root locus plots the poles of the closed loop transfer function in the complex s-plane as a function of a gain parameter (see pole–zero plot).
The CCPR (component and component-plus-residual) plot is a refinement of the partial residual plot, adding ^ . This is the "component" part of the plot and is intended to show where the "fitted line" would lie.
Data filtering: Use either R code or a drag-and-drop GUI to select cases of interest. Full data editing with one-click recoding; full undo / redo functionality, Compute columns via R code (e.g. via row-wise functions like rowMean, rowMeanNaRm, rowSum, rowSD ...) or a drag-and-drop GUI to create new variables or compute them from existing ones.
The PPCC plot is formed by: Vertical axis: Probability plot correlation coefficient; Horizontal axis: Value of shape parameter. That is, for a series of values of the shape parameter, the correlation coefficient is computed for the probability plot associated with a given value of the shape parameter. These correlation coefficients are plotted ...
Instead of computing the RQA measures of the entire recurrence plot, they can be computed in small windows moving over the recurrence plot along the LOI. This provides time-dependent RQA measures which allow detecting, e.g., chaos-chaos transitions. [9] [1] Note: the choice of the size of the window can strongly influence the measure trend.