Timeline for Can you gain more information from derived variables compared to measured variables?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Nov 4, 2022 at 19:09 | history | edited | kjetil b halvorsen♦ |
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| Nov 4, 2022 at 19:09 | comment | added | kjetil b halvorsen♦ | I don't think this question is about information in the sense of information-theory! | |
| Jun 9, 2020 at 19:38 | comment | added | Angela P | I agree that you can gain different insights with component variables derived from PCA, but the derived variables I am referring to are variables derived from the actual measured variables, which are then fed into the PCA process to obtain new component variables. For example instead of using distance of line A and B in the PCA (2 variables), use 3 variables: distance A minus overlapping distance, overlapping distance, and distance B minus overlapping distance in the PCA. Can additional variability be gained by the later method to distinguish scales into the geographical region via LDA? | |
| S Jun 9, 2020 at 15:03 | history | suggested | Thalassophile | CC BY-SA 4.0 |
fixed grammar
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| Jun 9, 2020 at 3:08 | review | Suggested edits | |||
| S Jun 9, 2020 at 15:03 | |||||
| Jun 9, 2020 at 0:06 | comment | added | AJKOER | In essence, one can, in my opinion, arrive at new insights with PCA, if one can frame the weighted index into a recognized concept. For example, tests on people to assess intelligence, which suggests combing measures relating to abstract thinking, or creativity. Then, new possibly better explanatory measures could be researched to develop the constructed concept more fully. Fish scales formations may relate to natural habitats,,,,,water's pH, a fishes' available food source, pollution, reduce O2 content (from warming trends) requiring greater speed. So, a new construct may relate to agility. | |
| Jun 8, 2020 at 21:00 | review | First posts | |||
| Jun 8, 2020 at 21:11 | |||||
| Jun 8, 2020 at 20:55 | history | asked | Angela P | CC BY-SA 4.0 |