en:learning:schools:s01:lecture-notes:ba-ln-13

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en:learning:schools:s01:lecture-notes:ba-ln-13 [2015/09/22 16:22] 127.0.0.1 external edit |
en:learning:schools:s01:lecture-notes:ba-ln-13 [2017/10/30 10:28] (current) aziegler |
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The following plots illustrate such kinds of data reduction and transformation. | The following plots illustrate such kinds of data reduction and transformation. | ||

- | <html><a href="https://www.flickr.com/photos/environmentalinformatics-marburg/15895707811" title="PCA by Environmental Informatics Marburg, on Flickr"><img src="https://farm8.staticflickr.com/7471/15895707811_3b2c5291e3_n.jpg" width="320" height="318" alt="PCA"></a></html> | + | {{ :en:learning:schools:s01:lecture-notes:15895707811_3b2c5291e3_n.jpg |}} |

The original value distribution along two variables called band a and band b is shown by the black dots. If, for example, a principal component analysis would be applied to this data set, a correlation matrix would be computed in order to find a function which transforms the data set onto itself from one multidimensional space into another one (i.e. eigentransformation). As a result, the new defined axis PC1 would describe the first dimension of this new multidimensional space. Graphically, it is drawn along the direction of maximum variance in the data set. Afterwards, a second axis (PC2) would be drawn along the direction of the maximum variance in the remaining data (i.e. the data points which have the same value on PC1). | The original value distribution along two variables called band a and band b is shown by the black dots. If, for example, a principal component analysis would be applied to this data set, a correlation matrix would be computed in order to find a function which transforms the data set onto itself from one multidimensional space into another one (i.e. eigentransformation). As a result, the new defined axis PC1 would describe the first dimension of this new multidimensional space. Graphically, it is drawn along the direction of maximum variance in the data set. Afterwards, a second axis (PC2) would be drawn along the direction of the maximum variance in the remaining data (i.e. the data points which have the same value on PC1). | ||

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In general, ordination results can be visualized in a two dimensional plot. | In general, ordination results can be visualized in a two dimensional plot. | ||

- | <html><a href="https://www.flickr.com/photos/bisfogo/15275724174" target="_blank" title="fc2002_ordination by BIS-Fogo, on Flickr"><img src="https://farm8.staticflickr.com/7465/15275724174_1b88295a3b.jpg" width="480" height="480" alt="fc2002_ordination"></a></html> | + | {{ :en:learning:schools:s01:lecture-notes:15275724174_1b88295a3b.jpg |}} |

The red/yellow points represent the locations of the individual field survey plots (this example is from a 2002 study) and the blue labels identify the location of the individual species within the newly created data space defined by axis CCA1 and CCA2. The arrows indicate the direction of the influence of the constraining variables. | The red/yellow points represent the locations of the individual field survey plots (this example is from a 2002 study) and the blue labels identify the location of the individual species within the newly created data space defined by axis CCA1 and CCA2. The arrows indicate the direction of the influence of the constraining variables. |

en/learning/schools/s01/lecture-notes/ba-ln-13.txt ยท Last modified: 2017/10/30 10:28 by aziegler

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