### Name: plot.boot
### Title: Plots of the Output of a Bootstrap Simulation
### Aliases: plot.boot
### Keywords: hplot nonparametric

### ** Examples

# We fit an exponential model to the air-conditioning data and use
# that for a parametric bootstrap.  Then we look at plots of the
# resampled means.
air.rg <- function(data, mle)
     rexp(length(data), 1/mle)

air.boot <- boot(aircondit$hours, mean, R=999, sim="parametric",
                 ran.gen=air.rg, mle=mean(aircondit$hours))
plot(air.boot)

# In the difference of means example for the last two series of the 
# gravity data
grav1 <- gravity[as.numeric(gravity[,2])>=7,]
grav.fun <- function(dat, w)
{    strata <- tapply(dat[, 2], as.numeric(dat[, 2]))
     d <- dat[, 1]
     ns <- tabulate(strata)
     w <- w/tapply(w, strata, sum)[strata]
     mns <- tapply(d * w, strata, sum)
     mn2 <- tapply(d * d * w, strata, sum)
     s2hat <- sum((mn2 - mns^2)/ns)
     c(mns[2]-mns[1],s2hat)
}

grav.boot <- boot(grav1, grav.fun, R=499, stype="w", strata=grav1[,2])
plot(grav.boot)
# now suppose we want to look at the studentized differences.
grav.z <- (grav.boot$t[,1]-grav.boot$t0[1])/sqrt(grav.boot$t[,2])
plot(grav.boot,t=grav.z,t0=0)

# In this example we look at the one of the partial correlations for the
# head dimensions in the dataset frets.
pcorr <- function( x )
{ 
#  Function to find the correlations and partial correlations between
#  the four measurements.
     v <- cor(x);
     v.d <- diag(var(x));
     iv <- solve(v);
     iv.d <- sqrt(diag(iv));
     iv <- - diag(1/iv.d) %*% iv %*% diag(1/iv.d);
     q <- NULL; 
     n <- nrow(v);
     for (i in 1:(n-1)) 
          q <- rbind( q, c(v[i,1:i],iv[i,(i+1):n]) );
     q <- rbind( q, v[n,] );
     diag(q) <- round(diag(q));
     q
}

frets.fun <- function( data, i )
{    d <- data[i,];
     v <- pcorr( d );
     c(v[1,],v[2,],v[3,],v[4,])
}
frets.boot <- boot(log(as.matrix(frets)), frets.fun, R=999)
plot(frets.boot, index=7, jack=TRUE, stinf=FALSE, useJ=FALSE)