discretize {entropy} | R Documentation |
discretize
puts observations from a continuous random variable
into bins and returns the corresponding vector of counts.
discretize2d
puts observations from a pair of continuous random variables
into bins and returns the corresponding table of counts.
discretize( x, numBins, r=range(x) ) discretize2d( x1, x2, numBins1, numBins2, r1=range(x1), r2=range(x2) )
x |
vector of observations. |
x1 |
vector of observations for the first random variable. |
x2 |
vector of observations for the second random variable. |
numBins |
number of bins. |
numBins1 |
number of bins for the first random variable. |
numBins2 |
number of bins for the second random variable. |
r |
range of the random variable (default: observed range). |
r1 |
range of the first random variable (default: observed range). |
r2 |
range of the second random variable (default: observed range). |
The bins for a random variable all have the same width. It is determined by the length of the range divided by the number of bins.
discretize
returns a vector containing the counts for each bin.
discretize2d
returns a matrix containing the counts for each bin.
Korbinian Strimmer (http://www.strimmerlab.org).
# load entropy library library("entropy") ### 1D example #### # sample from continuous uniform distribution x1 = runif(10000) hist(x1, xlim=c(0,1), freq=FALSE) # discretize into 10 categories y1 = discretize(x1, numBins=10, r=c(0,1)) y1 # compute entropy from counts entropy(y1) # empirical estimate near theoretical maximum log(10) # theoretical value for discrete uniform distribution with 10 bins # sample from a non-uniform distribution x2 = rbeta(10000, 750, 250) hist(x2, xlim=c(0,1), freq=FALSE) # discretize into 10 categories and estimate entropy y2 = discretize(x2, numBins=10, r=c(0,1)) y2 entropy(y2) # almost zero ### 2D example #### # two independent random variables x1 = runif(10000) x2 = runif(10000) y2d = discretize2d(x1, x2, numBins1=10, numBins2=10) sum(y2d) # joint entropy H12 = entropy(y2d ) H12 log(100) # theoretical maximum for 10x10 table # mutual information mi.empirical(y2d) # approximately zero # another way to compute mutual information # compute marginal entropies H1 = entropy(rowSums(y2d)) H2 = entropy(colSums(y2d)) H1+H2-H12 # mutual entropy