CVP_ADMMc {ADMMsigma} | R Documentation |
CV (no folds) ADMM penalized precision matrix estimation (c++)
Description
Cross validation (no folds) function for ADMMsigma. This function is to be used with CVP_ADMM.
Usage
CVP_ADMMc(n, S_train, S_valid, lam, alpha, diagonal = 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,
crit_cv = "loglik", start = "warm", trace = "progress")
Arguments
n |
sample size for X_valid (used to calculate crit_cv) |
S_train |
pxp sample covariance matrix for training data (denominator n). |
S_valid |
pxp sample covariance matrix for validation data (denominator n). |
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 ( |
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_abs |
absolute convergence tolerance. Defaults to 1e-4. |
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 |
crit_cv |
cross validation criterion ( |
start |
specify |
trace |
option to display progress of CV. Choose one of |
Value
cross validation errors (cv_crit)