associate | R Documentation |
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.
associate(m1, m2, parallel = FALSE, n_cor = 1, estimator = "values", d1, d2, p11 = 0, p01 = 0, p10 = 0)
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. |
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})
matrix of correlation values.
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)
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