Description Usage Arguments Details Value Author(s) References Examples
View source: R/FMA.concurrent.R
This function performs functional mediation regression under the concurrent model with given tuning parameter.
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Z |
a data matrix. |
M |
a data matrix. |
Y |
a data matrix. |
intercept |
a logic variable. Default is |
basis |
a data matrix. Basis function used in the functional data analysis. The number of columns is the number of basis function considered. If |
Ld2.basis |
a data matrix. The second derivative of the basis function. The number of columns is the number of basis function considered. If |
basis.type |
a character of basis function type. Default is Fourier basis ( |
nbasis |
an integer, the number of basis function included. If |
timeinv |
a numeric vector of length two, the time interval considered in the analysis. Default is (0,1). |
timegrids |
a numeric vector of time grids of measurement. If |
lambda.m |
a numeric value of the tuning parameter in the mediator model. |
lambda.y |
a numeric value of the tuning parameter in the outcome model. |
The concurrent mediation model is
M(t)=Z(t)α(t)+ε_{1}(t),
Y(t)=Z(t)γ(t)+M(t)β(t)+ε_{2}(t),
where α(t), β(t), γ(t) are coefficient curves. The model coefficient curves are estimated by minimizing the penalized L_{2}-loss.
basis |
the basis functions used in the analysis. |
M |
a list of output for the mediator model
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Y |
a list of output for the outcome model
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IE |
a list of output for the indirect effect comparing Z_{1}(t)=1 versus Z_{0}(t)=0
|
DE |
a list of output for the direct effect comparing Z_{1}(t)=1 versus Z_{0}(t)=0
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Yi Zhao, Johns Hopkins University, zhaoyi1026@gmail.com;
Xi Luo, Brown University xi.rossi.luo@gmail.com;
Martin Lindquist, Johns Hopkins University, mal2053@gmail.com;
Brian Caffo, Johns Hopkins University, bcaffo@gmail.com
Zhao et al. (2017). Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data. arXiv preprint arXiv:1805.06923.
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# Concurrent functional mediation model
data(env.concurrent)
Z<-get("Z",env.concurrent)
M<-get("M",env.concurrent)
Y<-get("Y",env.concurrent)
# consider Fourier basis
fit<-FMA.concurrent(Z,M,Y,intercept=FALSE,timeinv=c(0,300))
# estimate of alpha
plot(fit$M$curve[1,],type="l",lwd=5)
lines(get("alpha",env.concurrent),lty=2,lwd=2,col=2)
# estimate of gamma
plot(fit$Y$curve[1,],type="l",lwd=5)
lines(get("gamma",env.concurrent),lty=2,lwd=2,col=2)
# estimate of beta
plot(fit$Y$curve[2,],type="l",lwd=5)
lines(get("beta",env.concurrent),lty=2,lwd=2,col=2)
# estimate of causal curves
plot(fit$IE$curve,type="l",lwd=5)
plot(fit$DE$curve,type="l",lwd=5)
##################################################
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