
library(BART)

B <- getOption('mc.cores', 1)
figures = getOption('figures', default='NONE')

##simulate from Friedman's five-dimensional test function
##Friedman JH. Multivariate adaptive regression splines
##(with discussion and a rejoinder by the author).
##Annals of Statistics 1991; 19:1-67.

f = function(x) #only the first 5 matter
    sin(pi*x[ , 1]*x[ , 2]) + 2*(x[ , 3]-.5)^2+x[ , 4]+0.5*x[ , 5]-1.5

sigma = 1.0  #y = f(x) + sigma*z where z~N(0, 1)
k = 50       #number of covariates
thin = 10
ndpost = 1000
nskip = 100

par(mfrow=c(2, 2))

for(n in c(200, 1000, 10000)) {
    set.seed(12)
    x.train=matrix(runif(n*k), n, k)
    Ey.train = f(x.train)
    y.train=(Ey.train+sigma*rnorm(n)>0)*1

    ##run BART with B cores in parallel
    mc.train = mc.pbart(x.train, y.train, mc.cores=B, keepevery=thin,
                        seed=99, ndpost=ndpost, nskip=nskip)

    x <- x.train

    x4 <- seq(0, 1, length.out=10)

    for(i in 1:10) {
        x[ , 4] <- x4[i]

        if(i==1) x.test <- x
        else x.test <- rbind(x.test, x)
    }

    ##run predict with B cores in parallel
    mc.test <- predict(mc.train, newdata=x.test, mc.cores=B)

    ##create Friedman's partial dependence function for x4
    pred <- matrix(nrow=ndpost, ncol=10)

    for(i in 1:10) {
        h <- (i-1)*n+1:n
        pred[ , i] <- apply(mc.test$prob.test[ , h], 1, mean)
        ##pred[ , i] <- apply(pnorm(mc.test[ , h]), 1, mean)
    }

    pred <- apply(pred, 2, mean)

    plot(x4, qnorm(pred), xlab='x4', ylab='partial dependence function', type='l')

    geweke <- gewekediag(mc.train$yhat.train)

    i <- floor(seq(1, n, length.out=10))

    auto.corr <- acf(mc.train$yhat.train[ , i], plot=FALSE)
    max.lag <- max(auto.corr$lag[ , 1, 1])

    j <- seq(-0.5, 0.4, length.out=10)
    for(h in 1:10) {
        if(h==1)
            plot(1:max.lag+j[h], auto.corr$acf[1+(1:max.lag), h, h],
                 type='h', xlim=c(0, max.lag+1), ylim=c(-1, 1),
                 ylab='acf', xlab='lag')
        else
            lines(1:max.lag+j[h], auto.corr$acf[1+(1:max.lag), h, h],
                 type='h', col=h)
    }

    for(j in 1:10) {
        if(j==1)
            plot(pnorm(mc.train$yhat.train[ , i[j]]),
                 type='l', ylim=c(0, 1),
                 sub=paste0('N:', n, ', k:', k),
                 ylab=expression(Phi(f(x))), xlab='m')
        else
            lines(pnorm(mc.train$yhat.train[ , i[j]]),
                 type='l', col=j)
    }

    j <- -10^(log10(n)-1)
    plot(geweke$z, pch='.', cex=2, ylab='z', xlab='i',
         sub=paste0('N:', n, ', k:', k),
         xlim=c(j, n), ylim=c(-5, 5))
    lines(1:n, rep(-1.96, n), type='l', col=6)
    lines(1:n, rep(+1.96, n), type='l', col=6)
    lines(1:n, rep(-2.576, n), type='l', col=5)
    lines(1:n, rep(+2.576, n), type='l', col=5)
    lines(1:n, rep(-3.291, n), type='l', col=4)
    lines(1:n, rep(+3.291, n), type='l', col=4)
    lines(1:n, rep(-3.891, n), type='l', col=3)
    lines(1:n, rep(+3.891, n), type='l', col=3)
    lines(1:n, rep(-4.417, n), type='l', col=2)
    lines(1:n, rep(+4.417, n), type='l', col=2)
    text(c(1, 1), c(-1.96, 1.96), pos=2, cex=0.6, labels='0.95')
    text(c(1, 1), c(-2.576, 2.576), pos=2, cex=0.6, labels='0.99')
    text(c(1, 1), c(-3.291, 3.291), pos=2, cex=0.6, labels='0.999')
    text(c(1, 1), c(-3.891, 3.891), pos=2, cex=0.6, labels='0.9999')
    text(c(1, 1), c(-4.417, 4.417), pos=2, cex=0.6, labels='0.99999')

    if(figures!='NONE')
    dev.copy2pdf(file=paste(figures, paste0('geweke-pbart2-', n, '.pdf'),
                            sep='/'))
}

par(mfrow=c(1, 1))
