| entropy {immunarch} | R Documentation |
Compute information-based estimates and distances.
entropy(.data, .base = 2, .norm = FALSE, .do.norm = NA, .laplace = 1e-12)
kl_div(.alpha, .beta, .base = 2, .do.norm = NA, .laplace = 1e-12)
js_div(.alpha, .beta, .base = 2, .do.norm = NA, .laplace = 1e-12, .norm.entropy = FALSE)
cross_entropy(.alpha, .beta, .base = 2, .do.norm = NA,
.laplace = 1e-12, .norm.entropy = FALSE)
.data |
Numeric vector. Any distribution. |
.base |
Numeric. A base of logarithm. |
.norm |
Logical. If TRUE then normalise the entropy by the maximal value of the entropy. |
.do.norm |
If TRUE then normalise input distributions to make them sum up to 1. |
.laplace |
Numeric. A value for the laplace correction. |
.alpha |
Numeric vector. A distribution of some random value. |
.beta |
Numeric vector. A distribution of some random value. |
.norm.entropy |
Logical. If TRUE then normalise the resultant value by the average entropy of input distributions. |
A numeric value.
P <- abs(rnorm(10)) Q <- abs(rnorm(10)) entropy(P) kl_div(P, Q) js_div(P, Q) cross_entropy(P, Q)