All functions
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cv.sparse.mediation()
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Conduct K-fold cross validation for sparse mediation with elastic net with multiple tuning parameters |
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ginv.largep()
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Compute inverse, squareroot and inverse of the square root of the covariance |
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sparse.mediation()
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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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sparse.mediation.largep_omega()
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Conduct sparse mediation for parge p ( p > n) with L1 or L2 penalization using fast computation of inverse matrix |
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sparse.mediation.largep_omega0()
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Conduct sparse mediation for parge p ( p > n) with L1 or L2 penalization using fast computation of inverse matrix |
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sparse.mediation.old()
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Conduct sparse mediation with elastic net (Old version) |
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sqrtmat.comp()
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Compute squareroot of large covariance matrix |