CV_ADMMc {ADMMsigma} | R Documentation |
CV ADMM penalized precision matrix estimation (c++)
Description
Cross validation function for ADMMsigma.
Usage
CV_ADMMc(X, S, lam, alpha, diagonal = FALSE, path = FALSE, rho = 2,
mu = 10, tau_inc = 2, tau_dec = 2, crit = "ADMM", tol_abs = 1e-04,
tol_rel = 1e-04, maxit = 10000L, adjmaxit = 10000L, K = 5L,
crit_cv = "loglik", start = "warm", trace = "progress")
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 elastic net penalty. If a vector of parameters is provided, they should be in increasing order. |
alpha |
elastic net mixing parameter contained in [0, 1]. |
diagonal |
option to penalize the diagonal elements of the estimated precision matrix ( |
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. |
rho |
initial step size for ADMM algorithm. |
mu |
factor for primal and residual norms in the ADMM algorithm. This will be used to adjust the step size |
tau_inc |
factor in which to increase step size |
tau_dec |
factor in which to decrease step size |
crit |
criterion for convergence ( |
tol_rel |
relative convergence tolerance. Defaults to 1e-4. |
maxit |
maximum number of iterations. Defaults to 1e4. |
adjmaxit |
adjusted maximum number of iterations. During cross validation this option allows the user to adjust the maximum number of iterations after the first |
K |
specify the number of folds for cross validation. |
crit_cv |
cross validation criterion ( |
start |
specify |
trace |
option to display progress of CV. Choose one of |
Value
list of returns includes:
lam |
optimal tuning parameter. |
alpha |
optimal tuning parameter. |
path |
array containing the solution path. Solutions will be ordered in ascending alpha values for each lambda. |
min.error |
minimum average cross validation error (cv_crit) for optimal parameters. |
avg.error |
average cross validation error (cv_crit) across all folds. |
cv.error |
cross validation errors (cv_crit). |