mazeinda (version 0.0.2)

associate: 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.

Value

matrix of correlation values.

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 \(p_{11}^2 t_{11} + 2 * (p_{00} p_{11} - p_{01} p_{10})\)

Examples

# NOT RUN {
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)
# }