Description Usage Arguments Details Value Author(s) References Examples
View source: R/FMA.historical.boot.R
This function performs functional mediation regression under the historical influence model with given tuning parameter. Point-wise confidence bands are obtained from bootstrap.
1 2 3 4 5 6 7 | FMA.historical.boot(Z, M, Y, delta.grid1 = 1, delta.grid2 = 1, delta.grid3 = 1,
intercept = TRUE, basis1 = NULL, Ld2.basis1 = NULL, basis2 = NULL, Ld2.basis2 = NULL,
basis.type = c("fourier"), nbasis1 = 3, nbasis2 = 3,
timeinv = c(0, 1), timegrids = NULL,
lambda1.m = 0.01, lambda2.m = 0.01, lambda1.y = 0.01, lambda2.y = 0.01,
sims = 1000, boot = TRUE, boot.ci.type = c("bca", "perc"),
conf.level = 0.95, verbose = TRUE)
|
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.m |
a numeric vector of tuning parameter values on the s domain in the mediator model. |
lambda2.m |
a numeric vector of tuning parameter values on the t domain in the mediator model. |
lambda1.y |
a numeric vector of tuning parameter values on the s domain in the outcome model. |
lambda2.y |
a numeric vector of tuning parameter values on the t domain in the outcome model. |
sims |
an integer indicating the number of simulations for inference. |
boot |
a logical value, indicating whether or not bootstrap should be used. Default is |
boot.ci.type |
a character of confidence interval method. |
conf.level |
a number of significance level. Default is 0.95. |
verbose |
a logical value, indicating whether print out bootstrap replications. |
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.
alpha |
a list of output for α estimate
|
gamma |
a list of output for γ estimate
|
beta |
a list of output for β estimate
|
IE |
a list of output for indirect effect estimate
|
DE |
a list of output for direct effect estimate
|
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ##################################################
# Historical influence functional mediation model
data(env.historical)
Z<-get("Z",env.historical)
M<-get("M",env.historical)
Y<-get("Y",env.historical)
# consider Fourier basis
fit.boot<-FMA.historical.boot(Z,M,Y,delta.grid1=3,delta.grid2=3,delta.grid3=3,
intercept=FALSE,timeinv=c(0,300))
##################################################
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