combine {mazeinda}

combine

Description

Designed to combine the matrix of correlation values with the matrix of p-values so that in the cases when the null hypothesis cannot be rejected with a level of confidence indicated by the significance, the correlation is set to zero. Thanks to the package foreach, computation can be done in parallel using the desired number of cores.

Usage

combine(m1, m2, sl = 0.05, 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.

sl

level of significance for testing the null hypothesis. Default is 0.05.

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 test pairwise monotonic associations of vectors within one set m, run combine(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_{01},p_{10}, p_{00} 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, if the estimator is 0, it does not imply that the vectors are independent.

Value

matrix of combined association values and p-values.