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en:similarity

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en:similarity [2019/02/09 19:42] David Zelený |
en:similarity [2019/02/23 07:44] David Zelený [Similarity, dissimilarity and distance] |
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There is a number of measures of similarities or distances ([[references|Legendre & Legendre 2012]] list around 30 of them). The first decision one has to make is whether the aim is R- or Q-mode analysis (R-mode focuses on differences among species, Q-mode on differences among samples), since some of the measures differ between both modes (e.g. Pearson's //r// correlation coefficient makes sense for association between species (R-mode), but not for association between samples (Q-mode); in contrast, e.g. Sørensen index can be used in both Q- and R-mode analysis, called Dice index in R-mode analysis). Further, if focusing on differences between samples (Q-mode), the most relevant measures in ecology are asymmetric indices ignoring double zeros (more about //double-zero problem// below). Then, it also depends whether the data are qualitative (i.e. binary, presence-absence) or quantitative (species abundances). In the case of distance indices, an important criterium is whether they are metric (they can be displayed in Euclidean space) or not, since this influences the choice of the index for some ordination or clustering methods. | There is a number of measures of similarities or distances ([[references|Legendre & Legendre 2012]] list around 30 of them). The first decision one has to make is whether the aim is R- or Q-mode analysis (R-mode focuses on differences among species, Q-mode on differences among samples), since some of the measures differ between both modes (e.g. Pearson's //r// correlation coefficient makes sense for association between species (R-mode), but not for association between samples (Q-mode); in contrast, e.g. Sørensen index can be used in both Q- and R-mode analysis, called Dice index in R-mode analysis). Further, if focusing on differences between samples (Q-mode), the most relevant measures in ecology are asymmetric indices ignoring double zeros (more about //double-zero problem// below). Then, it also depends whether the data are qualitative (i.e. binary, presence-absence) or quantitative (species abundances). In the case of distance indices, an important criterium is whether they are metric (they can be displayed in Euclidean space) or not, since this influences the choice of the index for some ordination or clustering methods. | ||

- | [[references|Legendre & Legendre (2012)]] offers a kind of "key" how to select an appropriate measure for given data and problem (Tables 7.4-7.6). Generally, as a rule of thumb, Bray-Curtis and Hellinger distances are better choices than Euclidean or Chi-square distances. | + | [[references|Legendre & Legendre (2012)]] offers a key how to select an appropriate measure for given data and problem (check their Tables 7.4-7.6). Generally, as a rule of thumb, Bray-Curtis and Hellinger distances are better choices than Euclidean or Chi-square distances. |

===== Double-zero problem ===== | ===== Double-zero problem ===== |

en/similarity.txt · Last modified: 2019/02/26 22:08 by David Zelený