pchc (version 1.3)

Correlation between pairs of variables: Correlation between pairs of variables

Description

Correlations between pairs of variables.

Usage

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

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.

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.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

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

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, cortest, pcor

Examples

x <- matrix( rnorm(100 * 100), ncol = 100)
y <- matrix( rnorm(100 * 100), ncol = 100)
system.time( a <- corpairs(x, y) )