It's common to use a shaded region or error bars to depict uncertainty in plots. However, shaded regions and error bars tend to have definite extents, which rarely represents the reality of uncertainty. In truth, gaussian uncertainty tapers gradually, and should be depicted accordingly.
Too often we try to depict a binary world in which all values are either within the range of uncertainty, or outside the range of uncertainty. This function is meant to show that all errors are *not* contained within the arbitrary bounds of one standard deviation of the noise. To accomplish this, the errorshade function creates a semitransparent RGB image of a specified color, whose opacity scales directly with a gaussian distribution of uncertainty.
Citation pour cette source
Chad Greene (2024). errorshade (https://www.mathworks.com/matlabcentral/fileexchange/58335-errorshade), MATLAB Central File Exchange. Récupéré le .
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- MATLAB > Graphics > 2-D and 3-D Plots > Data Distribution Plots > Histograms >
- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Descriptive Statistics and Visualization > Statistical Visualization > Box Plots >
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Remerciements
Inspiré par : confplot, JackKnife, errorfill, confplot_t, raacampbell/shadedErrorBar, boundedline.m, stdshade, plot_ci, Plot with shadings for e.g. standard error, Shaded Error Bar YY, mseb(x,y,errBar,lineProps,transparent), superbar
A inspiré : Prakyathkantharaju/Monte-Carlo-simulation
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Version | Publié le | Notes de version | |
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1.1.0.0 | Fixed a typo in calculation of Gaussian distribution, added moving average option.
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1.0.0.0 |