Description Usage Arguments Value
Cross validation (no folds) function for GLASSO. This function is to be used with CVP_GLASSO.
1 2 3 4 | CVP_GLASSOc(n, S_train, S_valid, lam, diagonal = FALSE, crit_out = "avg",
crit_in = "loss", tol_out = 1e-04, tol_in = 1e-04, maxit_out = 10000L,
maxit_in = 10000L, adjmaxit_out = 10000L, crit_cv = "loglik",
start = "warm", trace = "progress")
|
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. |
diagonal |
option to penalize the diagonal elements of the estimated precision matrix (Ω). Defaults to |
crit_out |
criterion for convergence in outer (blockwise) loop. Criterion |
crit_in |
criterion for convergence in inner (lasso) loop. Criterion for convergence. Criterion |
tol_out |
convergence tolerance for outer (blockwise) loop. Defaults to 1e-4. |
tol_in |
convergence tolerance for inner (lasso) loop. Defaults to 1e-4. |
maxit_out |
maximum number of iterations for outer (blockwise) loop. Defaults to 1e4. |
maxit_in |
maximum number of iterations for inner (lasso) loop. Defaults to 1e4. |
adjmaxit_out |
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 |
cross validation errors (crit_cv)
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