# test_associations: test_associations In mazeinda: Monotonic Association on Zero-Inflated Data

## 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

 ```1 2``` ```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.

## 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.

## Value

matrix of p-values of association.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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) ```

mazeinda documentation built on May 2, 2019, 3:25 a.m.