Estimate controlled direct effect for BKMR
CDE.bkmr(
a,
astar,
e.y,
m.value = NULL,
m.quant = c(0.1, 0.5, 0.75),
fit.y,
alpha = 0.05,
sel,
seed
)exposure variables at current level
exposure variables at counterfactual level
effect modifier for the outcome variable
values that the mediator is set to
values of the quantile that the mediator is set to
model fit regressing outcome on exposures, effect modifiers, mediator and confounders on outcome
1-confidence interval
a vector selecting which iterations of thfe fit should be retained or inference
the random seed to use to evaluate the code
Controlled direct effect for BKMR
For guided examples, go to https://zc2326.github.io/causalbkmr/articles/BKMRCMA_QuickStart.html
if (FALSE) {
library(causalbkmr)
dat <- cma_sampledata(N=300, L=3, P=3, scenario=1, seed=7)
A <- cbind(dat$z1, dat$z2, dat$z3)
X <- cbind(dat$x3)
y <- dat$y
m <- dat$M
E.M <- NULL
E.Y <- dat$x2
Z.M <- cbind(A,E.M)
Z.Y <- cbind(A, E.Y)
Zm.Y <- cbind(Z.Y, m)
set.seed(1)
fit.y <- kmbayes(y=y, Z=Zm.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y,file="bkmr_y.RData")
set.seed(2)
fit.y.TE <- kmbayes(y=y, Z=Z.Y, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.y.TE,file="bkmr_y_TE.RData")
set.seed(3)
fit.m <- kmbayes(y=m, Z=Z.M, X=X, iter=5000, verbose=TRUE, varsel=FALSE)
#save(fit.m,file="bkmr_m.RData")
X.predict <- matrix(colMeans(X),nrow=1)
astar <- c(apply(A, 2, quantile, probs=0.25))
a <- c(apply(A, 2, quantile, probs=0.75))
e.y10 = quantile(E.Y, probs=0.1)
e.y90 = quantile(E.Y, probs=0.9)
sel<-seq(2500,5000,by=5)
CDE.ey10 <- CDE.bkmr(a=a, astar=astar, e.y = e.y10, m.quant=c(0.1,0.5,0.75),
fit.y=fit.y, alpha=0.05, sel=sel, seed=777)
CDE.ey10$est
plotdf <- as.data.frame(CDE.ey10$est)
plotdf["Effect"] <- rownames(plotdf)
ggplot(plotdf, aes(Effect, mean, ymin = lower, ymax = upper )) +
geom_pointrange(position = position_dodge(width = 0.75)) + coord_flip()
}