sparse.mediation: Conduct sparse mediation with elastic net

Description Usage Arguments Details Value Author(s) References Examples

View source: R/sparse.mediation.R

Description

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.

Usage

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sparse.mediation(X, M, Y, tol = 10^(-5), max.iter = 50, lambda = log(1 +
  (1:30)/100), alpha = 1, tau = 1)

Arguments

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

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)

figure

(defult=NULL) print figures for mean predictive errors by tuning parameters alpha and lambda

glmnet.penalty.factor

(default=c(0,rep(1,2*V))) give different weight of penalization for the 2V mediation paths.

Details

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.

Value

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

Author(s)

Seonjoo Lee, [email protected]

References

TBA

Examples

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N=100
V=50
set.seed(1234)
a = rbinom(V,1,0.1)*5;b<-a
X = rnorm(N)
M =  X %*% t(a)+ matrix(rnorm(N*V),N,V)
Y =  X + M %*% b + rnorm(N)
sparse.mediation(X,M,Y,tol=10^(-10),max.iter=100,lambda = log(1+(1:25)/50))

seonjoo/sparsemediation documentation built on May 13, 2018, 2:53 a.m.