mazeinda (version 0.0.2)

test_associations: test_associations

Description

To test pairwise monotonic associations of vectors within one set \(m\), run test_associations(\(m\),\(m\)). Note that the values on the diagonal will not be necessarily significant if the vectors contain 0's, as it can be seen by the formula \(p_{11}^2 t_{11} + 2 * (p_{00} p_{11} - p_{01} p_{10})\). The formula for the variance of the estimator proposed by Pimentel(2009) does not apply in case \(p_{11}\), \(p_{00}\), \(p_{01}\), \(p_{10}\) attain the values 0 or 1. In these cases the R function cor.test is used. Note that while independence implies that the estimator is 0, the estimator being 0 does not imply that the vectors are independent.

Usage

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

Arguments

m1, m2

matrices whose columns are used to estimate the \(p_{ij}\) parameters. If no estimation calculations are needed, default is NA. Both are necessary if cross-correlating pairwise the vectors from two datasets.

parallel

should the computations for combiing 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 p-values of association.

Details

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.

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)
test_associations(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)
test_associations(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)
test_associations(m1,m2)
m3= matrix(abs(rnorm(36)),6)
m4= matrix(abs(rnorm(36)),6)
test_associations(m3,m4)
# }