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# Get confidence intervals for the effects of the various treatments
sibp_amce<-function(sibp.fit, X, Y, G = NULL, seed = 0, level = 0.05, thresh = 0.5){
# Want it to be the case that G %*% beta selects the correct beta
if (is.null(G)){
G <- matrix(1, nrow = nrow(X), ncol = 1)
}
set.seed(seed)
G.test <- G[sibp.fit$test.ind,,drop=FALSE]
Z.test <- infer_Z(sibp.fit, X)
Y.test <- (Y[sibp.fit$test.ind] - sibp.fit$meanY)/sibp.fit$sdY
Z.hard <- apply(Z.test, 2, function(z) sapply(z, function(zi) ifelse(zi >= thresh, 1, 0)))
L <- sibp.fit$L
K <- sibp.fit$K
if (L == 1){
fit <- lm(Y.test ~ Z.hard)
} else{
rhsmat <- c()
for (l in 1:L){
rhsmat<-cbind(rhsmat,Z.hard*G.test[,l])
}
fit <- lm(Y.test~-1+as.matrix(G.test)+rhsmat)
}
ci.bounds <- cbind(coef(fit)+qnorm(level/2)*summary(fit)$coefficients[,2],
coef(fit)+qnorm(1-level/2)*summary(fit)$coefficients[,2])
cidf <- data.frame(x = 1:((K+1)*L),
effect = coef(fit),
L = ci.bounds[,1],
U = ci.bounds[,2])
cidf[,-1] <- cidf[,-1]*sibp.fit$sdY
sibp.amce <- cidf
if (L == 1){
rownames(sibp.amce) <- c("Intercept", paste0("Z",1:K))
}
if (L > 1){
rnames <- c()
for (i in 1:(K+1)){
for (l in 1:L){
if (i == 1){
rnames <- c(rnames, paste0("Intercept:",colnames(G)[l]))
}
else{
rnames <- c(rnames, paste0("Z",i-1,":",colnames(G)[l]))
}
}
}
rownames(sibp.amce) <- rnames
}
return(sibp.amce)
}
sibp_amce_plot<-function(sibp.amce, xlab = "Feature", ylab = "Outcome", subs = NULL){
if(is.null(subs)){
subs <- 1:nrow(sibp.amce)
}
x <- sibp.amce[subs,]$x
effect <- sibp.amce[subs,]$effect
U <- sibp.amce[subs,]$U
L <- sibp.amce[subs,]$L
ggplot(sibp.amce[subs,], aes(x = x, y = effect)) + geom_errorbar(aes(ymax=U, ymin=L)) + geom_point(size = 5) +
theme(axis.text=element_text(size=14), axis.title=element_text(size=16,face="bold"),
axis.title.x=element_text(vjust=-0.25)) +
labs(x = "Feature", y = "Outcome") + geom_hline(yintercept = 0, linetype = 2)
}
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