Description Usage Arguments Details Value Author(s) References Examples
View source: R/FMA.historical.CV.R
This function performs functional mediation regression under the historical influence model. Tuning parameter is chosen based on cross-validation.
1 2 3 4 |
Z |
a data matrix. |
M |
a data matrix. |
Y |
a data matrix. |
delta.grid1 |
a number indicates the width of treatment-mediator time interval in the mediator model. |
delta.grid2 |
a number indicates the width of treatment-outcome time interval in the outcome model. |
delta.grid3 |
a number indicates the width of mediator-outcome time interval in the outcome model. |
intercept |
a logic variable. Default is |
basis1 |
a data matrix. Basis function on the s domain used in the functional data analysis. The number of columns is the number of basis function considered. If |
Ld2.basis1 |
a data matrix. The second derivative of the basis function on the s domain. The number of columns is the number of basis function considered. If |
basis2 |
a data matrix. Basis function on the t domain used in the functional data analysis. The number of columns is the number of basis function considered. If |
Ld2.basis2 |
a data matrix. The second derivative of the basis function on the t domain. The number of columns is the number of basis function considered. If |
basis.type |
a character of basis function type. Default is Fourier basis ( |
nbasis1 |
an integer, the number of basis function on the s domain included. If |
nbasis2 |
an integer, the number of basis function on the t domain 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 |
lambda1 |
a numeric vector of tuning parameter values on the s domain. |
lambda2 |
a numeric vector of tuning parameter values on the t domain. |
nfolds |
a number gives the number of folds in cross-validation. |
The historical influence mediation model is
M(t)=\int_{Ω_{t}^{1}}Z(s)α(s,t)ds+ε_{1}(t),
Y(t)=\int_{Ω_{t}^{2}}Z(s)γ(s,t)ds+\int_{Ω_{t}^{3}}M(s)β(s,t)ds+ε_{2}(t),
where α(s,t), β(s,t), γ(s,t) are coefficient curves; Ω_{t}^{j}=[(t-δ_{j})\vee 0,t] for j=1,2,3. The model coefficient curves are estimated by minimizing the penalized L_{2}-loss. Tuning parameter λ controls the smoothness of the estimated curves, and is chosen by cross-validation.
basis1 |
the basis functions on the s domain used in the analysis. |
basis2 |
the basis functions on the t domain 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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# Historical influence functional mediation model
data(env.historical)
Z<-get("Z",env.historical)
M<-get("M",env.historical)
Y<-get("Y",env.historical)
## Not run:
# consider Fourier basis
fit<-FMA.historical.CV(Z,M,Y,delta.grid1=3,delta.grid2=3,delta.grid3=3,
intercept=FALSE,timeinv=c(0,300))
## End(Not run)
##################################################
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