entropy: Information measures.
In tcR: Advanced Data Analysis of Immune Receptor Repertoires

Description Usage Arguments Value See Also

Functions for information measures of and between distributions of values.

Warning! Functions will check if .data if a distribution of random variable (sum == 1) or not. To force normalisation and / or to prevent this, set .do.norm to TRUE (do normalisation) or FALSE (don't do normalisation). For js.div and kl.div vectors of values must have equal length.

Functions:

- The Shannon entropy quantifies the uncertainty (entropy or degree of surprise) associated with this prediction.

- Kullback-Leibler divergence (information gain, information divergence, relative entropy, KLIC) is a non-symmetric measure of the difference between two probability distributions P and Q (measure of information lost when Q is used to approximate P).

- Jensen-Shannon divergence is a symmetric version of KLIC. Square root of this is a metric often referred to as Jensen-Shannon distance.

entropy(.data, .norm = F, .do.norm = NA, .laplace = 1e-12)

kl.div(.alpha, .beta, .do.norm = NA, .laplace = 1e-12)

js.div(.alpha, .beta, .do.norm = NA, .laplace = 1e-12, .norm.entropy = F)

`.data, .alpha, .beta`	Vector of values.
`.norm`	if T then compute normalised entropy (H / Hmax).
`.do.norm`	One of the three values - NA, T or F. If NA than check for distrubution `(sum(.data) == 1)`. and normalise if needed with the given laplace correction value. if T then do normalisation and laplace correction. If F than don't do normalisaton and laplace correction.
`.laplace`	Value for Laplace correction which will be added to every value in the .data.
`.norm.entropy`	if T then normalise JS-divergence by entropy.