Timeline for What are some popular choices for visualizing 4-dimensional data?
Current License: CC BY-SA 4.0
11 events
| when toggle format | what | by | license | comment | |
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| Jun 11, 2020 at 14:32 | history | edited | CommunityBot |
Commonmark migration
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| S Apr 24, 2019 at 8:45 | history | suggested | Glorfindel | CC BY-SA 4.0 |
broken image fixed (click 'rendered output' or 'side-by-side' to see the difference); for more info, see https://gist.github.com/Glorfindel83/9d954d34385d2ac2597bbe864466259f
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| Apr 24, 2019 at 7:57 | review | Suggested edits | |||
| S Apr 24, 2019 at 8:45 | |||||
| S Sep 17, 2017 at 10:01 | history | edited | itdxer | CC BY-SA 3.0 |
Added link that work
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| S Sep 17, 2017 at 10:01 | history | suggested | Y123 | CC BY-SA 3.0 |
Link broken
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| Sep 17, 2017 at 8:27 | review | Suggested edits | |||
| S Sep 17, 2017 at 10:01 | |||||
| Nov 22, 2014 at 1:32 | comment | added | Dianne Cook | Hey, that's what the quesiton asked %^) | |
| Nov 20, 2014 at 17:47 | comment | added | mklingen | @DianneCook that's true. I guess that's what I get for always working with smooth, continuous 3D volumetric data ;) | |
| Nov 20, 2014 at 13:46 | comment | added | Dianne Cook | The colored 3D scatterplot is only really suitable for continuous functions on 3D data. If the gradient of the function changes smoothly then you can see some pattern across the point scatter. Similarly the volume visualization at bottom works best in this scenario too. If the function is very noisy you will have a hard time seeing anything. If you have 4 explanatory variables (like for doing PCA or clustering) plotting 3 in Euclidean coordinates and the 4th using some nonlinear mapping to color in introducing some perceptual bias, which can't be quantified. | |
| Nov 18, 2014 at 22:58 | history | edited | mklingen | CC BY-SA 3.0 |
added 235 characters in body
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| Nov 18, 2014 at 22:44 | history | answered | mklingen | CC BY-SA 3.0 |