Description Usage Arguments Value Author(s) References Examples
Sparse mediation for high-dimensional mediators
Fit a mediation model via penalized maximum likelihood and structural equation model. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. Currently, mediation analysis is developed based on gaussian assumption.
Multiple Mediaton Model: (1) M = Xa + e1 (2) Y = Xc' + Mb + e2 And in the optimization, we do not regularize c', due to the assumption of partial mediation.
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X |
One-dimensional predictor |
M |
Multivariate mediator |
Y |
Outcome |
tol |
(default -10^(-5)) convergence criterion |
max.iter |
(default=100) maximum iteration |
lambda |
(default=log(1+(1:50)/125)) tuning parameter for L1 penalization |
lambda2 |
(default=c(0.2,0.5)) tuning parameter for L1 penalization for covariance matrix, used only for p>n. |
alpha |
(default=1) tuning parameter for L2 penalization |
tau |
(default=1) tuning parameter for differentail weight between paths a (X -> M) and b (M -> Y) |
verbose |
(default=TRUE) print progress. |
Omega.out |
(defult=TRUE) output Omega estimates |
c: directeffect per each tuning parameter lambda. length(lambda)-dimensional vector
hatb: Path b (M->Y given X) estimates: V-by-lenbth(lambda) matrix
hata: Path a (X->M) estimates: V-by-lenbth(lambda) matrix
medest: Mediation estimates (a*b): V-by-lenbth(lambda) matrix
alpha: a scalor of the numing parameter for L2 regularization
lambda: a vector of tuning parameters for L1-penalization
tau: weight used.
nump: Number of selected mediation paths
Omega Estimated covariance matrix of the mediator
Seonjoo Lee, sl3670@cumc.columbia.edu
TBA
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