cv_err_binom: Use cross validation paths to choose a value of gamma for...
In jean997/jadeTF:

This function requires having a jade_path object for the original data and for data with missing folds. See cv_fit0.

1 2	cv_err_binom(orig.path, reads, cv.path.list = NULL, use.converged.only = TRUE, control.l1 = TRUE, margin = 0.01)

`orig.path`	Either a path object or a file containing a `jade_path` object.
`reads`	A matrix of reads corresponding to the data.
`cv.path.list`	Either a list of `jade_path` objects or a vector of file names containing `jade_path` objects for the cross validation data sets.
`use.converged.only`	Only use fits which have converged.
`control.l1`	Only use fits with `l1.total <= l1.total0`.

A list with elements #'

l1.total: A vector of length N where N is the number of fits in orig.path giving the total L1 distance between all pairs of profiles. Equivalent to orig.path$l1.total.
cv.err.l1: An n.folds by N matrix where N is the number of fits in orig.path. Each row gives the average corss validation error for each value of l1.total.
err.l1: A vector of length N giving average cross validation error over all folds. Equivalent to colSums(cv.err.l1) with missing values for fits that were discarded according to specification of use.converged.only and control.l1.
err.se.l1: Estimated standard error of err.l1.
cv.min.l1,cv.1se.l1: Indices of the fits in orig.path corresponding to the minimum and 1 standard error rule cross validation error.