partial.r {psych} | R Documentation |
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.
partial.r(m, x, y)
m |
A data or correlation matrix |
x |
The variable numbers associated with the X set. |
y |
The variable numbers associated with the Y set |
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.
The matrix of partial correlations.
William Revelle
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/
mat.regress
for a similar application for regression
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