Contents
What is double zero problem?
Double-zero problem “Double zero” is a situation when certain species is missing in both compared community samples for which similarity/distance is calculated. The fact that the mesic species is missing does not say anything about ecological similarity or difference between both samples.
In what circumstances is the Bray Curtis preferred to the Euclidean distance?
In the most simple way this is: if you have two samples with 10 variables sharing the same measures in three of them they will have a similarity of 3 and a distance of seven. Euclidian distance is best for variables with continuous data while Bray Curtis is best for categorical or binary data.
What is euclidean distance ecology?
In brief euclidean distance simple measures the distance between 2 points but it does not take species identity into account. Thus Euclidean distance can give you a situation where you have two sites that share all the same species being farther apart (less similar) than two sites that don’t share any species.
How do you interpret Bray Curtis dissimilarity?
The Bray–Curtis dissimilarity is bounded between 0 and 1, where 0 means the two sites have the same composition (that is they share all the species), and 1 means the two sites do not share any species. At sites with where BC is intermediate (e.g. BC = 0.5) this index differs from other commonly used indices.
What is a double zero in algebra?
Strategic Advice: A double zero occurs in a polynomial when a factor is repeated, or in other words, squared. For example, the factor (x – a) produces a simple zero at x = a, while (x – b)2 produces a double zero at x = b.
What is the purpose of the Sørenson index?
The Sørensen–Dice coefficient (see below for other names) is a statistic used to gauge the similarity of two samples. It was independently developed by the botanists Thorvald Sørensen and Lee Raymond Dice, who published in 1948 and 1945 respectively.
Why is Euclidean distance not good for sparse data?
If you increase the dimensionality and sparseness enough so that it holds for almost all attributes, the difference will be minimal. Even worse: if you normalized your vectors to have length | | x | | = 1, then the euclidean distance of any two objects will be √2 with high probability.
When does Euclidean distance become weakly discriminant?
I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data vectors where the Euclidean distance does not perform well?
Is the Euclidean distance of two objects useful?
Even worse: if you normalized your vectors to have length | | x | | = 1, then the euclidean distance of any two objects will be √2 with high probability. So as a rule of thumb, for Euclidean distance to be usable (I’m not claiming useful or meaningful) the objects should be non-zero in 3 / 4 of attributes.
Is it good to use Euclidean distance for multivariate normal?
This is true for multivariate normal and even when the components are IID (spherical normal). But if you want to strictly speak about Euclidean distance even in low dimensional space if the data have a correlation structure Euclidean distance is not the appropriate metric.