psych (version 2.6.9)

RV: Three measures of the correlations between sets of variables

Description

How to measure the the correlation between two clusters or groups of variables x and y from the same data set is a recurring problem. Perhaps the most obvious is simply the unweighted correlation Ru.

Consider the matrix M composed of four submatrices

	Rx	Rxy
M =	Rxy	Ry

The unit weighted correlation, Ru is merely

\(Ru =\frac{\Sigma{r_{xy}}}{\sqrt{\Sigma{r_x}\Sigma{r_y}} }\)

A second is the Set correlation (also found in lmCor) by Cohen 1982) which is
\(Rset = 1- \frac{det(m)}{det(x)* det(y)}\)
Where m is the full matrix (x+y)by (x+y). and det represents the determinant.

A third approach (the RV coeffiecent) was introduced by Escoufier (1970) and Robert and Escoufier (1976).
\(RV = \frac{tr(xy (xy)')}{\sqrt{(tr(x x') * tr(y y'))}}\).

Where tr is the trace operator. (The sum of the diagonals).

The analysis can be done from the raw data or from correlation or covariance matrices. From the raw data, just specify the x and y variables. If using correlation/covariance matrixes, the xy matrix must be specified as well.

If using raw data, just specify the x and y columns and the data file.

Usage

RV(x, y, xy = NULL, data=NULL, cor = "cor",correct=0)

Value

RV

The RV statistic

Rset

Cohen's set correlation

Ru

The unit weighted correlation between x and y.

Rx

The correlation matrix of the x variables.

Ry

The correlation matrix of the y variables.

Rxy

The intercorrelations of x and y.

Details

If using raw data, just specify the columns in x and y. If using a correlation matrix or covariance matrix, the inter corrlations/covariances) are specified in xy. The results match those of the RV function from matrixCalculations and the coeffRV function from factoMineR.

References

P. Robert and Y. Escoufier, 1976, A Unifying Tool for Linear Multivariate Statistical Methods: The RV- Coefficient. Journal of the Royal Statistical Society. Series C (Applied Statistics), Volume 25, pp. 257-265.

J. Cohen (1982) Set correlation as a general multivariate data-analytic method. Multivariate Behavioral Research, 17(3):301-341.

See Also

lmCor for unit weighted correlations.

Examples

#from raw data
RV (attitude[1:3],attitude[4:7])  #find the correlations
RV (attitude[1:3],attitude[4:7],cor="cov")

#find the correlations
R <- cor(attitude)
r1 <- R[1:3,1:3]
r2 <- R[4:7,4:7]
r12 <- R[1:3,4:7]
RV(r1,r2,r12)

#or find the covariances
C <- cov(attitude)
c1 <- C[1:3,1:3]
c2 <- C[4:7,4:7]
c12 <- C[1:3,4:7]
RV(c1, c2, c12)