R: Correlation between pairs of variables

Correlation between pairs of variables {Rfast} | R Documentation

## Correlation between pairs of variables

### Description

Correlations between pairs of variables.

### Usage

    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.

### Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr> and Manos Papadakis <papadakm95@gmail.com>.

### 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.

### See Also

`correls, allbetas, mvbetas`

### Examples

    x <- matrnorm(100, 100)
    y <- matrnorm(100, 100)
    system.time( corpairs(x, y) )
    a <- corpairs(x, y)
    x <- NULL
    y <- NULL

[Package Rfast version 2.0.0 Index]