File:Data-Driven-Estimation-of-Imputation-Error—A-Strategy-for-Imputation-with-a-Reject-Option-pone.0164464.s006.ogv
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[edit]DescriptionData-Driven-Estimation-of-Imputation-Error—A-Strategy-for-Imputation-with-a-Reject-Option-pone.0164464.s006.ogv |
English: Effect of Threshold, Kidney disease dataset. Movie showing the effect of different thresholds (see Fig 5). A-C. Visualization of the effect of threshold using the complete cases with simulated missing values for A. Probabilistic PCA (PPCA) imputation, B. Mean imputation, C. KNN imputation. In all three, blue represent complete cases. Simulated cases with missing that would be rejected (red) or accepted (green) for the current threshold. The size represents the actual imputation error. D. Threshold versus an errors examplified with Root Mean Square Errors (RMSE) of the actual imputation error for the included cases (full red lines) and the excludedcases (dotted blue lines). |
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Source | S4 Video from Bak N, Hansen L (2016). "Data Driven Estimation of Imputation Error—A Strategy for Imputation with a Reject Option". PLOS ONE. DOI:10.1371/journal.pone.0164464. PMID 27723782. PMC: 5056679. | ||
Author | Bak N, Hansen L | ||
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This file is licensed under the Creative Commons Attribution 4.0 International license.
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 03:17, 29 October 2016 | 20 s, 1,600 × 830 (586 KB) | Open Access Media Importer Bot (talk | contribs) | Automatically uploaded media file from Open Access source. Please report problems or suggestions here. |
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Short title | Effect of Threshold, Kidney disease dataset. |
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Author | Bak N, Hansen L |
Usage terms | http://creativecommons.org/licenses/by/4.0/ |
Image title | Movie showing the effect of different thresholds (see Fig 5). A-C. Visualization of the effect of threshold using the complete cases with simulated missing values for A. Probabilistic PCA (PPCA) imputation, B. Mean imputation, C. KNN imputation. In all three, blue represent complete cases. Simulated cases with missing that would be rejected (red) or accepted (green) for the current threshold. The size represents the actual imputation error. D. Threshold versus an errors examplified with Root Mean Square Errors (RMSE) of the actual imputation error for the included cases (full red lines) and the excludedcases (dotted blue lines). |
Software used | Xiph.Org libtheora 1.1 20090822 (Thusnelda) |
Date and time of digitizing | 2016 |
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2016
application/ogg
253a09eb1920e7ff3b2dffcabf6770e8deebab9f
599,897 byte
20 second
830 pixel
1,600 pixel
Categories:
- Videos of cognitive science
- Videos of cognitive psychology
- Videos of social sciences
- Videos of diagnostic medicine
- Diagnostic radiology
- Videos of medical ultrasound
- Medical imaging
- Multivariate analysis
- Principal component analysis
- Videos of physical sciences
- Videos of artificial intelligence
- Chronic kidney diseases
- Research assessment
- Research errors
- Media from PLOS ONE