associate {mazeinda}

Associate pairwise vectors form one or two sets

Description

Given two matrices m_1 and m_2, computes all pairwise correlations of each vector in m_1 with each vector in m_2. Thanks to the package foreach, computation can be done in parallel using the desired number of cores.

Usage

associate(m1, m2, parallel = FALSE, n_cor = 1, estimator = "values", d1,
  d2, p11 = 0, p01 = 0, p10 = 0)

Arguments

m1, m2

matrices whose columns are to be correlated. If no estimation calculations are needed, default is NA.

parallel

should the computations for associating the matrices be done in parallel? Default is FALSE

n_cor

number of cores to be used if the computation is run in parallel. Default is 1

estimator

string indicating how the parameters p_{11}, p_{01}, p_{10}, p_{00} are to be estimated. The default is 'values', which indicates that they are estimated based on the entries of x and y. If estimates=='mean', each p_{ij} is estimated as the mean of all pairs of column vectors in m_1, and of m_2 if needed. If estimates=='own', the p_{ij}'s must be given as arguments.

d1, d2

sets of vectors used to estimate p_{ij} parameters. If just one set is needed set d_1=d_2.

p11

probability that a bivariate observation is of the type (m,n), where m,n>0.

p01

probability that a bivariate observation is of the type (0,n), where n>0.

p10

probability that a bivariate observation is of the type (n,0), where n>0.

Details

To find pairwise monotonic associations of vectors within one set m, run associate(m,m). Note that the values on the diagonal will not be necessarely 1 if the vectors contain 0's, as it can be seen by the formula p_{11}^2 t_{11} + 2 * (p_{00} p_{11} - p_{01} p_{10})

Value

matrix of correlation values.

Examples

v1=c(0,0,10,0,0,12,2,1,0,0,0,0,0,1)
v2=c(0,1,1,0,0,0,1,1,64,3,4,2,32,0)
associate(v1,v2)
m1=matrix(c(0,0,10,0,0,12,2,1,0,0,0,0,0,1,1,64,3,4,2,32,0,0,43,54,3,0,0,3,20,1),6)
associate(m1,m1)
m2=matrix(c(0,1,1,0,0,0,1,1,64,3,4,2,32,0,0,43,54,3,0,0,3,20,10,0,0,12,2,1,0,0),6)
associate(m1,m2)