# corpairs: Correlation between pairs of variables In Rfast: A Collection of Efficient and Extremely Fast R Functions

## Description

Correlations between pairs of variables.

## Usage

 `1` ```corpairs(x, y, rho = NULL, logged = FALSE, parallel = FALSE) ```

## Arguments

 `x` A matrix with real valued data. `y` A matrix with real valued data whose dimensions match those of x. `rho` This can be a vector of assumed correlations (equal to the number of variables or the columns of x or y) to be tested. If this is not the case, leave it NULL and only the correlations will be returned. `logged` Should the p-values be returned (FALSE) or their logarithm (TRUE)? This is taken into account only if "rho" is a vector. `parallel` Should parallel implentations take place in C++? The default value is FALSE.

## Details

The paired correlations are calculated. For each column of the matrices x and y the correlation between them is calculated.

## Value

A vector of correlations in the case of "rho" being NULL, or a matrix with two extra columns, the test statistic and the (logged) p-value.

Michail Tsagris

## References

Lambert Diane (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics. 34(1):1-14.

Johnson Norman L., Kotz Samuel and Kemp Adrienne W. (1992). Univariate Discrete Distributions (2nd ed.). Wiley

Cohen, A. Clifford (1960). Estimating parameters in a conditional Poisson distribution. Biometrics. 16:203-211.

Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). Univariate Discrete Distributions (third edition). Hoboken, NJ: Wiley-Interscience.

``` correls, allbetas, mvbetas ```
 ```1 2 3 4 5 6``` ```x <- matrnorm(100, 100) y <- matrnorm(100, 100) system.time( corpairs(x, y) ) a <- corpairs(x, y) x <- NULL y <- NULL ```