generalCorr (version 1.2.0)

parcor_linear: Partial correlation coefficient between Xi and Xj after removing the linear effect of all others.

Description

This function uses a symmetric correlation matrix R as input to compute usual partial correlations between \(X_i\) and \(X_j\) where j can be any one of the remaining variables. Computation removes the effect of all other variables in the matrix. The user is encouraged to remove all known irrelevant rows and columns from the R matrix before submitting it to this function.

Usage

parcor_linear(x, i, j)

Arguments

x

Input a p by p matrix R of symmetric correlation coefficients.

i

A column number identifying the first variable.

j

A column number identifying the second variable.

Value

ouij

Partial correlation Xi with Xj after removing all other X's

ouji

Partial correlation Xj with Xi after removing all other X's

myk

A list of column numbers whose effect has been removed

See Also

See parcor_ijk for generalized partial correlation coefficients useful for causal path determinations.

Examples

# NOT RUN {
# }
# NOT RUN {
set.seed(34);x=matrix(sample(1:600)[1:99],ncol=3)
colnames(x)=c('V1', 'v2', 'V3')
c1=cor(x)
parcor_linear(c1, 2,3)
# }
# NOT RUN {
# }