Description Usage Arguments Details Value See Also Examples

Converts a covariance matrix to a correlation matrix and then identifies variables which should be dropped due to high dependence.

1 | ```
cov_offdiag_check(cov, cut_point = 0.9999)
``` |

`cov` |
a matrix. |

`cut_point` |
threshold which signifies two variables are highly dependent and one should be omitted. Specifically, when a group of variables are highly dependent, only the first variable is kept and all others are dropped. Defaults to 0.9999. |

Intended to be a helper function for flagging highly dependent variables which cause the covariance matrix to be singular or near singular; and hence, non-invertible.

`remove_vars`

locations of variables to be dropped.`remove_length`

number of variables to be dropped.`remove_text`

a message detailing an action taken by the covariance check wrapper function`cov_check()`

.

1 2 3 4 | ```
cov1 <- matrix(c(1,0,0,0,1,1,0,1,1),ncol=3,nrow=3)
cov2 <- diag(c(1,1,1))
cov_offdiag_check(cov1, cut_point = 0.9999)
cov_offdiag_check(cov2, cut_point = 0.9999)
``` |

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