ccorPvals {CMO}R Documentation

Calculate p-value for canonical correlation values.

Description

Calculates p-values for canonical correlation values

Usage

ccorPvals(ccor, N, onlyPs = TRUE)

Arguments

ccor A List object as derived by the function cancor.
N number of independent observations the canonical correlation has been derived from
onlyPs TRUE limits the output to a vector of p-values

Details

Canonical correlation analysis is a way of making sense of cross-covariance matrices between two sets of random variables. The canonical correlation values can be converted into a test statistic of Chi-square type to derive a significance measurement (p-value) foreach canonical correlation value.

Value

cor correlations derived by the function cancor
lambda cumulative product of the noise vector of the correlation vector
chisq Chi-square value foreach correlation
df degrees of freedom of the Chi-square distribution
pvals p-values computed foreach correlation value using the Chi-square distribution

Author(s)

Christian Montel

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New SLanguage.Wadsworth & Brooks/Cole.

Hotelling H. (1936). Relations between two sets of variables. Biometrika, 28, p. 321–327.

Seber, G. A. F. (1984). Multivariate Observations. New York: Wiley, p. 506f.

Pedhazur E. (1997). Multiple regression in behavioral research Orlando: Harcourt Brace College Publishers, p. 939f.

See Also

cancor

Examples

pop <- LifeCycleSavings[,2:3]
oec <- LifeCycleSavings[,-(2:3)]
ccor <- cancor(pop,oec)
ccorPvals(ccor,N=dim(LifeCycleSavings)[1],onlyPs=FALSE)

[Package CMO version 1.02 Index]