#our calls always this style: mediate(model.m, model.y, treat= "X", mediator="M", sims = nSimImai)
#both models are lm with no special types / conditions
stripped.down.mediate <-
function(model.m, model.y, sims = 1000, treat = "treat.name", mediator = "med.name",
conf.level = .95, control.value = 0, treat.value = 1){
# Model frames for M and Y models
m.data <- model.frame(model.m) # Call.M$data
y.data <- model.frame(model.y) # Call.Y$data
# Specify group names
group.m <- NULL
group.y <- NULL
group.out <- NULL
group.id.m <- NULL
group.id.y <- NULL
group.id <- NULL
group.name <- NULL
# Numbers of observations and categories
n.m <- nrow(m.data)
n.y <- nrow(y.data)
if(n.m != n.y){
stop("number of observations do not match between mediator and outcome models")
} else{
n <- n.m
}
m <- length(sort(unique(model.frame(model.m)[,1])))
# Extracting weights from models
weights.m <- model.weights(m.data)
weights.y <- model.weights(y.data)
weights.m <- rep(1,nrow(m.data))
weights.y <- rep(1,nrow(y.data))
weights <- weights.m
cat.0 <- control.value
cat.1 <- treat.value
########################################################################
## Case I-1: Quasi-Bayesian Monte Carlo
########################################################################
# Get mean and variance parameters for mediator simulations
MModel.coef <- coef(model.m)
scalesim.m <- FALSE
MModel.var.cov <- vcov(model.m)
YModel.coef <- coef(model.y)
scalesim.y <- FALSE
YModel.var.cov <- vcov(model.y)
if(sum(is.na(MModel.coef)) > 0){
stop("NA in model coefficients; rerun models with nonsingular design matrix")
}
MModel <- mvtnorm::rmvnorm(sims, mean=MModel.coef, sigma=MModel.var.cov)
if(sum(is.na(YModel.coef)) > 0){
stop("NA in model coefficients; rerun models with nonsingular design matrix")
}
YModel <- mvtnorm::rmvnorm(sims, mean=YModel.coef, sigma=YModel.var.cov)
#####################################
## Mediator Predictions
#####################################
pred.data.t <- pred.data.c <- m.data
pred.data.t[,treat] <- cat.1
pred.data.c[,treat] <- cat.0
mmat.t <- model.matrix(terms(model.m), data=pred.data.t)
mmat.c <- model.matrix(terms(model.m), data=pred.data.c)
sigma <- summary(model.m)$sigma
error <- rnorm(sims*n, mean=0, sd=sigma)
muM1 <- tcrossprod(MModel, mmat.t)
muM0 <- tcrossprod(MModel, mmat.c)
PredictM1 <- muM1 + matrix(error, nrow=sims)
PredictM0 <- muM0 + matrix(error, nrow=sims)
rm(error)
rm(mmat.t, mmat.c)
#####################################
## Outcome Predictions
#####################################
effects.tmp <- array(NA, dim = c(n, sims, 4))
for(e in 1:4){
tt <- switch(e, c(1,1,1,0), c(0,0,1,0), c(1,0,1,1), c(1,0,0,0))
Pr1 <- matrix(nrow=n, ncol=sims)
Pr0 <- matrix(nrow=n, ncol=sims)
for(j in 1:sims){
pred.data.t <- pred.data.c <- y.data
# Set treatment values
cat.t <- ifelse(tt[1], cat.1, cat.0)
cat.c <- ifelse(tt[2], cat.1, cat.0)
cat.t.ctrl <- ifelse(tt[1], cat.0, cat.1)
cat.c.ctrl <- ifelse(tt[2], cat.0, cat.1)
pred.data.t[,treat] <- cat.t
pred.data.c[,treat] <- cat.c
# Set mediator values
PredictMt <- PredictM1[j,] * tt[3] + PredictM0[j,] * (1 - tt[3])
PredictMc <- PredictM1[j,] * tt[4] + PredictM0[j,] * (1 - tt[4])
pred.data.t[,mediator] <- PredictMt
pred.data.c[,mediator] <- PredictMc
ymat.t <- model.matrix(terms(model.y), data=pred.data.t)
ymat.c <- model.matrix(terms(model.y), data=pred.data.c)
Pr1[,j] <- t(as.matrix(YModel[j,])) %*% t(ymat.t)
Pr0[,j] <- t(as.matrix(YModel[j,])) %*% t(ymat.c)
rm(ymat.t, ymat.c, pred.data.t, pred.data.c)
}
effects.tmp[,,e] <- Pr1 - Pr0 ### e=1:mediation(1); e=2:mediation(0); e=3:direct(1); e=4:direct(0)
rm(Pr1, Pr0)
}
rm(PredictM1, PredictM0, YModel, MModel)
et1<-effects.tmp[,,1] ### mediation effect (1)
et2<-effects.tmp[,,2] ### mediation effect (0)
et3<-effects.tmp[,,3] ### direct effect (1)
et4<-effects.tmp[,,4] ### direct effect (0)
delta.1 <- t(as.matrix(apply(et1, 2, weighted.mean, w=weights)))
delta.0 <- t(as.matrix(apply(et2, 2, weighted.mean, w=weights)))
zeta.1 <- t(as.matrix(apply(et3, 2, weighted.mean, w=weights)))
zeta.0 <- t(as.matrix(apply(et4, 2, weighted.mean, w=weights)))
rm(effects.tmp)
tau <- (zeta.1 + delta.0 + zeta.0 + delta.1)/2
nu.0 <- delta.0/tau
nu.1 <- delta.1/tau
delta.avg <- (delta.1 + delta.0)/2
zeta.avg <- (zeta.1 + zeta.0)/2
nu.avg <- (nu.1 + nu.0)/2
d0 <- mean(delta.0) # mediation effect
d1 <- mean(delta.1)
z1 <- mean(zeta.1) # direct effect
z0 <- mean(zeta.0)
tau.coef <- mean(tau) # total effect
n0 <- median(nu.0)
n1 <- median(nu.1)
d.avg <- (d0 + d1)/2
z.avg <- (z0 + z1)/2
n.avg <- (n0 + n1)/2
########################################################################
## Compute Outputs and Put Them Together
########################################################################
low <- (1 - conf.level)/2
high <- 1 - low
d0.ci <- quantile(delta.0, c(low,high), na.rm=TRUE)
d1.ci <- quantile(delta.1, c(low,high), na.rm=TRUE)
tau.ci <- quantile(tau, c(low,high), na.rm=TRUE)
z1.ci <- quantile(zeta.1, c(low,high), na.rm=TRUE)
z0.ci <- quantile(zeta.0, c(low,high), na.rm=TRUE)
n0.ci <- quantile(nu.0, c(low,high), na.rm=TRUE)
n1.ci <- quantile(nu.1, c(low,high), na.rm=TRUE)
d.avg.ci <- quantile(delta.avg, c(low,high), na.rm=TRUE)
z.avg.ci <- quantile(zeta.avg, c(low,high), na.rm=TRUE)
n.avg.ci <- quantile(nu.avg, c(low,high), na.rm=TRUE)
# p-values
d0.p <- mediation::pval(delta.0, d0)
d1.p <- mediation::pval(delta.1, d1)
d.avg.p <- mediation::pval(delta.avg, d.avg)
z0.p <- mediation::pval(zeta.0, z0)
z1.p <- mediation::pval(zeta.1, z1)
z.avg.p <- mediation::pval(zeta.avg, z.avg)
n0.p <- mediation::pval(nu.0, n0)
n1.p <- mediation::pval(nu.1, n1)
n.avg.p <- mediation::pval(nu.avg, n.avg)
tau.p <- mediation::pval(tau, tau.coef)
# Detect whether models include T-M interaction
INT <- paste(treat,mediator,sep=":") %in% attr(terms(model.y),"term.labels") |
paste(mediator,treat,sep=":") %in% attr(terms(model.y),"term.labels")
return(SimpleMediateResult(direct_p = z.avg.p, indirect_p=d.avg.p))
}
export_environment <- function(env){
glob_env = globalenv()
for(item in names(env)){
glob_env[[item]] = env[[item]]
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.