d1char.d1nat: Probability Mass Function Calculator for Array of Characters

In stheoreme: Klimontovich's S-Theorem Algorithm Implementation and Data Preparation Tools

Description

Function d1char.d1nat is applied to a pair of vectors of characters or to a pair of 1d arrays of characters and generates then the pair of corresponding probability vectors by calling d1nat function

Usage

d1char.d1nat(farr0, farr1, reject = c(""))

Arguments

farr0	vector of characters
farr1	vector of characters
reject	vector of rejected characters (the characters excluded from original vectors for analysis)

Details

First, it assigns the identification number for each individual character met in original vectors. Then it works similarly to hist function but for the pair of samples with identification numbers as outcomes. It prints the decoding array (listing the identification numbers corresponded for each character) and, as a bonus, it prints basic statistics summary for distributions alongside with technical plot. It is recommended for use as a data preparation step before the following Klimontovich's S-theorem based analysis.

Value

f0	probability vector representing state0 of a system
f1	probability vector representing state1 of a system

Author(s)

Vitaly Efremov <[e-mail]>

See Also

crit.stheorem, cxds.stheorem, d1nat

Examples

s0<-c("?", "!", "1", "a", "b", "b", "s", "x", "y", "z", "z", "z")
s1<-c("1", "1", "2", "b", "b", "s", "s", "x", "y", "z", "z", "z", "z")
b<-d1char.d1nat(farr0=s0,farr1=s1); b

s0<-"three witches watch three swatch watches. which witch watch which swatch watch?"
s1<-"who discovered america five hundred years ago? a brave man! indeed he was! discovered!"
b<-d1char.d1nat(unlist(strsplit(s0,"")),unlist(strsplit(s1,"")), 
                reject=c("."," ","!","d","e")); b

#example of 2-step data analysis with Klimontovich's S-theorem
s0<-c("a","b",rep("c",9),rep("d",2),"e","f","g",rep("h",2),"i","j"); s0
s1<-c(rep("a",16), rep("c",35), rep("i",13)); s1

# step a. Create probability vectors. It seems that s0 has lower level
# of orderliness (Shannon entropy is higher)
b<-d1char.d1nat(s0,s1); b
# step b. Compare samples with Klimontovich's S-theorem. Renormalized entropy indicates 
# the opposite: s0 is more ordered and difference in Shannon 
# entropy values was due to just "thermodynamic noise"
crit.stheorem(b$f0,b$f1)
cxds.stheorem(b$f0,b$f1)

stheoreme documentation built on May 2, 2019, 9:33 a.m.