Adding confidence intervals to plot from simulation data in R - r

Question

I've created a probit simulation based on a likelihood function and simulation, all of which can be replicated with the code below.
This is the likelihood function:
probit.ll <- function(par,ytilde,x) {
a <- par[1]
b <- par[2]
return( -sum( pnorm(ytilde*(a + b*x),log=TRUE) ))
}
This is the function to do the estimates:
my.probit <- function(y,x) {
# use OLS to get start values
par <- lm(y~x)$coefficients
ytilde <- 2*y-1
# Run optim
res <- optim(par,probit.ll,hessian=TRUE,ytilde=ytilde,x=x)
# Return point estimates and SE based on the inverse of Hessian
names(res$par) <- c('a','b')
se=sqrt(diag(solve(res$hessian)))
names(se) <- c('a','b')
return(list(par=res$par,se=se,cov=solve(res$hessian)))
}
And this is the function to generate the simulated model:
probit.data <- function(N=100,a=1,b=1) {
x <- rnorm(N)
y.star <- a + b*x + rnorm(N)
y <- (y.star > 0)
return( as.data.frame(cbind(y,x,y.star)) )
}
This simulates an n size equal 100:
probit.data100 <- function(N=100,a=2,b=1) {
x <- rnorm(N)
y.star <- a + b*x + rnorm(N)
y <- (y.star > 0)
return( as.data.frame(cbind(y,x,y.star)) )
}
#predicted value
se.probit.phat100 <- function(x, par, V) {
z <- par[1] + par[2] * x
# Derivative of q w.r.t. alpha and beta
J <- c( dnorm(z), dnorm(z)*par[2] )
return( sqrt(t(J) %*% V %*% J) )
}
dat100 <- probit.data100()
res100 <- my.probit(dat100$y,dat100$x)
res100
This function below will calculate the confidence intervals based on a non-parametric bootstrap approach (notice the sample function being used):
N <- dim(probit.data(N=100, a=1, b=1))[1]
npb.par <- matrix(NA,100,2)
colnames(npb.par) <- c("alpha","beta")
npb.eystar <- matrix(NA,100,N)
for (t in 1:100) {
thisdta <- probit.data(N=100, a=1, b=1)[sample(1:N,N,replace=TRUE),]
npb.par[t,] <- my.probit(thisdta$y,thisdta$x)$par
}
This function below just cleans up the bootstrap output, and the confidence intervals are what I would like to plot:
processres <- function(simres) {
z <- t(apply(simres,2,function(x) { c(mean(x),median(x),sd(x),quantile(x,c(0.05,0.95))) } ))
rownames(z) <- colnames(simres)
colnames(z) <- c("mean","median","sd","5%","95%")
z
}
processres(npb.par)
I would like to plot a graph like this (the one below), but add confidence intervals based on the processres function above. How can these confidence intervals be added to the plot?
x <- seq(-5,5,length=100)
plot(x, pnorm(1 - 0.5*x), ty='l', lwd=2, bty='n', xlab='x', ylab="Pr(y=1)")
rug(dat100$x)
I'm also open to a different plot code and/or package. I just want a graph based on this simulation with added confidence intervals.
Thanks!

Answer 1

Here's a way to add a shaded CI based on simulation results:
UPDATE: this now plots the expected curve (i.e. using mean alpha & beta values), and correctly passes these means to rnorm.
x <- seq(-5,5,length=100)
plot(x, pnorm(1 - 0.5*x), ty='n', lwd=2, bty='n', xlab='x', ylab="Pr(y=1)",
xaxs = 'i', ylim=c(0, 1))
params <- processres(npb.par)
sims <- 100000
sim.mat <- matrix(NA, ncol=length(x), nrow=sims)
for (i in 1:sims) {
alpha <- rnorm(1, params[1, 1], params[1, 3])
beta <- rnorm(1, params[2, 1], params[2, 3])
sim.mat[i, ] <- pnorm(alpha - beta*x)
}
CI <- apply(sim.mat, 2, function(x) quantile(x, c(0.05, 0.95)))
polygon(c(x, rev(x)), c(CI[1, ], rev(CI[2, ])), col='gray', border=NA)
lines(x, pnorm(params[1, 1] - params[2, 1]*x), lwd=2)
rug(dat100$x)
box()