CV_RIDGEc {ADMMsigma} | R Documentation |
CV ridge penalized precision matrix estimation (c++)
Description
Cross validation function for RIDGEsigma.
Usage
CV_RIDGEc(X, S, lam, path = FALSE, K = 3L, trace = "none")
Arguments
X |
option to provide a nxp matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable. |
S |
option to provide a pxp sample covariance matrix (denominator n). If argument is |
lam |
positive tuning parameters for ridge penalty. If a vector of parameters is provided, they should be in increasing order. Defaults to grid of values |
path |
option to return the regularization path. This option should be used with extreme care if the dimension is large. If set to TRUE, cores will be set to 1 and errors and optimal tuning parameters will based on the full sample. Defaults to FALSE. |
K |
specify the number of folds for cross validation. |
trace |
option to display progress of CV. Choose one of |
Value
list of returns includes:
lam |
optimal tuning parameter. |
path |
array containing the solution path. Solutions are ordered dense to sparse. |
min.error |
minimum average cross validation error for optimal parameters. |
avg.error |
average cross validation error across all folds. |
cv.error |
cross validation errors (negative validation likelihood). |