View source: R/parcor_linear.R

parcor_linear | R Documentation |

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.

```
parcor_linear(x, i, j)
```

`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. |

`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 |

This function calls `minor`

, and `cofactor`

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

See `parcor_ijk`

for generalized partial
correlation coefficients useful for causal path determinations.

```
## 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)
```

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