partial.r: Find the partial correlations for a set (x) of variables with...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

A straightforward application of matrix algebra to remove the effect of the variables in the y set from the x set. Input may be either a data matrix or a correlation matrix. Variables in x and y are specified by location.

Usage

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partial.r(m, x, y)

Arguments

m

A data or correlation matrix

x

The variable numbers associated with the X set.

y

The variable numbers associated with the Y set

Details

It is sometimes convenient to partial the effect of a number of variables (e.g., sex, age, education) out of the correlations of another set of variables. This could be done laboriously by finding the residuals of various multiple correlations, and then correlating these residuals. The matrix algebra alternative is to do it directly. To find the confidence intervals and "significance" of the correlations, use the corr.p function with n = n - s where s is the numer of covariates.

Value

The matrix of partial correlations.

Author(s)

William Revelle

References

Revelle, W. (in prep) An introduction to psychometric theory with applications in R. To be published by Springer. (working draft available at http://personality-project.org/r/book/

See Also

mat.regress for a similar application for regression

Examples

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jen <- make.hierarchical()    #make up a correlation matrix 
round(jen[1:5,1:5],2)
par.r <- partial.r(jen,c(1,3,5),c(2,4))
cp <- corr.p(par.r,n=98)  #assumes the jen data based upon n =100.
print(cp,short=FALSE)  #show the confidence intervals as well

psych documentation built on May 19, 2017, 6:40 p.m.

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