# parcor_linear: Partial correlation coefficient between Xi and Xj after... In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

 parcor_linear R Documentation

## 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

### Note

This function calls `minor`, and `cofactor`

### Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY.

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

### Examples

``````
## 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)

## End(Not run)

``````

generalCorr documentation built on May 1, 2023, 9:06 a.m.