# Calculate a Covariance Matrix for the Normal Distribution from a Matrix of Joint Probabilities

### Description

Computes a covariance matrix for a normal distribution which
corresponds to a binary distribution with marginal probabilities given
by `diag(commonprob)`

and pairwise probabilities given by
`commonprob`

.

For the simulations the values of `simulvals`

are used.

If a non-valid covariance matrix is the result, the program stops with an error in the case of NA arguments and yields are warning message if the matrix is not positive definite.

### Usage

1 | ```
commonprob2sigma(commonprob, simulvals)
``` |

### Arguments

`commonprob` |
matrix of pairwise probabilities. |

`simulvals` |
array received by |

### Value

A covariance matrix is returned with the same dimensions as
`commonprob`

.

### Author(s)

Friedrich Leisch

### References

Friedrich Leisch, Andreas Weingessel and Kurt Hornik (1998). On the generation of correlated artificial binary data. Working Paper Series, SFB “Adaptive Information Systems and Modelling in Economics and Management Science”, Vienna University of Economics, http://www.wu-wien.ac.at/am

### See Also

`simul.commonprob`

### Examples

1 2 |