# Computation of partial correlation coefficients

### Description

This function computes the matrix of partial correlation coefficients based on the results of the corresponding regression models.

### Usage

1 | ```
Beta2parcor(Beta,verbose=FALSE)
``` |

### Arguments

`Beta` |
matrix of regression coefficients |

`verbose` |
print information on conflicting signs etc. Default is |

### Details

A well-known result (Whittaker, 1990) shows that the matrix of
partial correlation coefficients can be estimated by computing a least
squares regression model for each variable. If there are more
variables than observations, the least squares problem is ill-posed
and needs regularization. The matrix `Beta`

stores the regression
coefficients of any user-defined regression method. The function
`Beta2parcor`

computes the
corresponding matrix of partial correlations.

### Value

matrix of partial correlation coefficients

### Note

This is an internal function.

### Author(s)

Nicole Kraemer

### References

J. Whittaker (1990) "Graphical models in applied multivariate statistics", Wiley, New York.

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks with Gaussian Graphical Models", BMC Bioinformatics, 10:384

http://www.biomedcentral.com/1471-2105/10/384/

### See Also

`ridge.net`

, `adalasso.net`

,`pls.net`

### Examples

1 | ```
# this is an internal function and should not be called by the user
``` |