netCoin (version 2.1.0)

sim: Similarity matrix.

Description

It calculates a similarity/distance matrix from either an incidence data frame/matrix or a coin object.

Usage

sim(input, procedures="Jaccard", level=.95, distance=FALSE, 
    minimum=1, maximum=Inf, sort=FALSE, decreasing=FALSE, 
    weight = NULL, pairwise = FALSE)

Value

A similarity/distance matrix.

Arguments

input

a binary data frame or a coin object, let's say an R list composed by a number of scenarios ($n) and a coincidence matrix with frequencies ($f).

procedures

a vector of statistics of similarity. See details below.

level

confidence level

distance

convert the similarity matrix into a distance matrix

minimum

minimum frequency to obtain a similarity/distance measure.

maximum

maxium frequency to obtain a similarity/distance measure.

sort

sort the list according to the values of a statistic. See details below

decreasing

order in a decreasing way.

weight

a vector of weights. Optimal for data.framed tables

pairwise

Pairwise mode of handling missing values if TRUE. Listwise by default.

Author

Modesto Escobar, Department of Sociology and Communication, University of Salamanca. See https://sociocav.usal.es/blog/modesto-escobar/

Details

Possible measures in procedures are

• Frequencies (f), Relative frequencies (x), Conditional frequencies (i), Coincidence degree (cc), Probable degree (cp),

• Expected (e), Confidence interval (con)

• Matching (m), Rogers & Tanimoto (t), Gower (g), Sneath (s), Anderberg (and),

• Jaccard (j), Dice (d), antiDice (a), Ochiai (o), Kulczynski (k),

• Hamann (ham), Yule (y), Pearson (p), odds ratio (od), Rusell (r),

• Haberman (h), Z value of Haberman (z).

• Hypergeometric p greater value (hyp).

Examples

# From a random incidence matrix I(25X4)
I<-matrix(rbinom(100,1,.5),nrow=25,ncol=4,
   dimnames=list(NULL,c("A","B","C","D")))
sim(I)
#Same results
C<-coin(I)
sim(C)