CV {CVglasso} | R Documentation |
Parallel Cross Validation
Description
Parallel implementation of cross validation.
Usage
CV(X = NULL, S = NULL, lam = 10^seq(-2, 2, 0.2), diagonal = FALSE,
path = FALSE, tol = 1e-04, maxit = 10000, adjmaxit = NULL, K = 5,
crit.cv = c("loglik", "AIC", "BIC"), start = c("warm", "cold"),
cores = 1, trace = c("progress", "print", "none"), ...)
Arguments
X |
option to provide a nxp data 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. Defaults to grid of values |
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 must be set to 1 and errors and optimal tuning parameters will based on the full sample. Defaults to FALSE. |
tol |
convergence tolerance. Iterations will stop when the average absolute difference in parameter estimates in less than |
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 |
cores |
option to run CV in parallel. Defaults to |
trace |
option to display progress of CV. Choose one of |
... |
additional arguments to pass to |
Value
returns list of returns which includes:
lam |
optimal tuning parameter. |
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). |