Description Usage Arguments Value Author(s) References Examples
Conduct K-fold cross validation for sparse mediation with elastic net with multiple tuning parameters
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X |
One-dimensional predictor |
M |
Multivariate mediator |
Y |
Outcome |
tol |
(default -10^(-10)) convergence criterion |
K |
(default=5) number of cross-validation folds |
max.iter |
(default=100) maximum iteration |
lambda |
(default=log(1+(1:30)/100)) tuning parameter for L1 penalization |
lambda2 |
(default=c(0.2,0.5)) tuning parameter for inverse covariance matrix sparsity. Only used if n>(2*V). |
alpha |
(defult=1) tuning parameter for L2 penalization |
tau |
(default=1) tuning parameter for differential weight for L1 penalty. |
multicore |
(default=1) number of multicore |
seednum |
(default=10000) seed number for cross validation |
verbose |
(default=FALSE) |
cv.lambda: optimal lambda
cv.tau: optimal tau
cv.alpha: optimal tau
cv.mse: minimum MSE value
mse: Array of MSE, length(alpha) x length(lambda) x length (tau)
lambda: vector of lambda
tau: vector of tau used
alpha: vector of alpha used
z: cross-valication results
Seonjoo Lee, sl3670@cumc.columbia.edu
TBA
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