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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.