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<H2><A name=SECTION00312000000000000000>S examples for simple bootstraps</A> 
</H2><PRE>laws&lt;-scan()
576 3.39
635 3.30
558 2.81
578 3.03 
666 3.44
580 3.07
555 3.00
661 3.43
651 3.36
605 3.13
653 3.12
575 2.74
545 2.76
572 2.88
594 2.96


"boot.simple"&lt;-

function(data, fun, nboot, ...)
{
        n &lt;- nrow(as.matrix(data))
        res.boot &lt;- vector("list", length = nboot)
        indb &lt;- matrix(sample(n, size = n * nboot, T), nrow = nboot)
        for(b in (1:nboot)) {
                res &lt;- fun(indb[b,  ], ...)
                plot(res, xlab = "", ylab = "", lab = c(0, 0, 0))
                res.boot[[b]] &lt;- res
        }
        return(res.boot)
}

"boot"&lt;-

function(ind, nboot, fun, ...)
{
# Tibshirani's efficient Bootstrap
# If fun is function of a matrix or returns multiple values
# it has to be transformed before

        data &lt;- matrix(sample(ind, size = length(ind) * nboot, replace = T),
                nrow = nboot)
        return(apply(data, 1, fun, ...))
}

#Elementary example of using boot

corr&lt;- function(x, xdata)
{
        cor(xdata[x, 1], xdata[x, 2])
}
j1000 &lt;- boot(1:15, 1000, theta, laws)
hist(j1000 - 0.777)
q.bt &lt;- quantile(junk1000, probs = c(0.01, 0.99))
#With greek label
hist(junk1000 - 0.777, nclass = 40, xlab = "q* - q", font = 13)

#Example of Bootstrapping a Mann-Whitney statistic
"mw"&lt;-
function(x, y)
{
#Function that computes a Mann-Whitney statistic for two samples
        n &lt;- length(x)
        m &lt;- length(y)
        rks &lt;- rank(c(x, y))
        wx &lt;- sum(rks[1:n])
        ux &lt;- n * m + (m * (m + 1))/2 - wx
        mwx &lt;- 1 - ux/(m * n)
        return(mwx)
}

"mwbt"&lt;-
function(x, y, nboot = 100, fun = mw, ...)
{
        bts &lt;- rep(0, nboot)
        for(b in (1:nboot)) {
                xbts &lt;- sample(x, length(x), replace = T)
                ybts &lt;- sample(y, length(y), replace = T)
                bts[b] &lt;- fun(xbts, ybts, ...)
        }
        return(bts)
}

"mwxy"&lt;-
function(xy, n)
{
#       n &lt;- length(x)
        m &lt;- length(xy) - n
        rks &lt;- rank(xy)
        wx &lt;- sum(rks[1:n])
        ux &lt;- n * m + (m * (m + 1))/2 - wx
        mwx &lt;- 1 - ux/(m * n)
        return(mwx)
}
</PRE><BR>
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<ADDRESS>Susan Holmes 2002-01-12 </ADDRESS></BODY></HTML>

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