BOOTSTRP Bootstrap statistics.
    BOOTSTAT = BOOTSTRP(NBOOT,BOOTFUN,D1,...) draws NBOOT bootstrap data
    samples, computes statistics on each sample using the function BOOTFUN,
    and returns the results in the matrix BOOTSTATS.  NBOOT must be a
    positive integer.  BOOTFUN is a function handle specified with @.
    Each row of BOOTSTAT contains the results of applying BOOTFUN to one
    bootstrap sample.  If BOOTFUN returns a matrix or array, then this
    output is converted to a row vector for storage in BOOTSTAT.
 
    The third and later input arguments (D1,...) are data (scalars,
    column vectors, or matrices) that are used to create inputs to BOOTFUN.
    BOOTSTRP creates each bootstrap sample by sampling with replacement
    from the rows of the non-scalar data arguments (these must have the
    same number of rows).  Scalar data are passed to BOOTFUN unchanged.
 
    [BOOTSTAT,BOOTSAM] = BOOTSTRP(...) returns BOOTSAM, a matrix of indices
    into the rows of the extra arguments.  To get the output samples BOOTSAM
    without applying a function, set BOOTFUN to empty ([]).
 
    Examples:
 
    Compute a sample of 100 bootstrapped means of random samples taken from
    the vector Y, and plot an estimate of the density of these bootstrapped
    means:
       y = exprnd(5,100,1);
       m = bootstrp(100, @mean, y);
       [fi,xi] = ksdensity(m);
       plot(xi,fi);
 
    Compute a sample of 100 bootstrapped means and standard deviations of
    random samples taken from the vector Y, and plot the bootstrap estimate
    pairs:
       y = exprnd(5,100,1);
       stats = bootstrp(100, @(x) [mean(x) std(x)], y);
       plot(stats(:,1),stats(:,2),'o')
 
    Estimate the standard errors for a coefficient vector in a linear
    regression by bootstrapping residuals:
       load hald ingredients heat
       x = [ones(size(heat)), ingredients];
       y = heat;
       b = regress(y,x);
       yfit = x*b;
       resid = y - yfit;
       se = std(bootstrp(1000, @(bootr) regress(yfit+bootr,x), resid));
 
    Bootstrap a correlation coefficient standard error:
       load lawdata gpa lsat
       se = std(bootstrp(1000,@corr,gpa,lsat));
 
    See also random, randsample, hist, ksdensity.

    Reference page in Help browser
       doc bootstrp