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# calculate shannon-entropy -sum(freqs * log2(freqs)) [1] 0.940286 As a side note, the function entropy.empirical is in the entropy package where you set the units to log2 allowing some more flexibility. Example: entropy.empirical(freqs, unit="log2") [1] 0.940286
The entropy function allows to estimate entropy from observed counts by a variety of methods: method="ML":maximum likelihood, see entropy.empirical. method="MM":bias-corrected maximum likelihood, see entropy.MillerMadow. method="Jeffreys": entropy.Dirichlet with a=1/2. method="Laplace": entropy.Dirichlet with a=1.
Computes Shannon entropy and the mutual information of two variables. The entropy quantifies the expected value of the information contained in a vector. The mutual information is a quantity that measures the mutual dependence of the two random variables.
v = c(0,4,3,6,7,3,2,3,4,5) entropy(discretize(v, numBins = 8, r = c(0,7))) and I get. [1] 1.834372. jolly good. Now, the question is: assuming that the following is the algorithm used to calculate the entropy – taken from Wikipedia.
Implements various estimators of entropy for discrete random variables, including the shrinkage estimator by Hausser and Strimmer (2009), the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator. It also offers an R interface to the NSB estimator.
We simply compute the entropy of the root node (Species) using 1.5849625, then subtract the sum of the bin entropies weighted by the proportion of data they represent exactly as per the IG formula shown above. IG_numeric (iris, "Sepal.Length", "Species", bins=5) ## [1] 0.6402424.
Calculates the approximate or sample entropy of a time series. Usage. approx_entropy(ts, edim = 2, r = 0.2*sd(ts), elag = 1) sample_entropy(ts, edim = 2, r = 0.2*sd(ts), tau = 1) Arguments. Details. Approximate entropy was introduced to quantify the the amount of regularity and the unpredictability of fluctuations in a time series.
