_B_o_o_t_s_t_r_a_p-_t _c_o_n_f_i_d_e_n_c_e _l_i_m_i_t_s
	
	     boott(x,theta, ..., sdfun=MISSING,nbootsd=25,nboott=200,
	       VS=F v.nbootg=100,v.nbootsd=25,v.nboott=200,
	          perc=c(.001,.01,.025,.05,.10,.50,.90,.95,.975,
	              .99,.999))
	
	_A_r_g_u_m_e_n_t_s:
	
	            x: a vector containing the data. Nonparametric
	               bootstrap sampling is used. To bootstrap from
	               more complex data structures (e.g bivariate
	               data) see the last example below.
	
	        theta: function to be bootstrapped. Takes x as an
	               argument, and may take additional arguments
	               (see below and last example).
	
	        sdfun: optional name of function for computing stan-
	               dard deviation of theta based on data x.
	               Should be of the form: sdmean <-
	               function(x,nbootsd,theta,...) where nbootsd
	               is a dummy argument that is not used. If
	               theta is the mean, for example, sdmean <-
	               function(x,nbootsd,theta,...)
	               sqrt(var(x)/length(x)).  If sdfun is missing,
	               then boott uses an inner bootstrap loop to
	               estimate the standard deviation of theta(x)
	
	      nbootsd: The number of bootstrap samples used to esti-
	               mate the standard deviation of theta(x)
	
	       nboott: The number of bootstrap samples used to esti-
	               mate the distribution of the bootstrap T
	               statistic.  200 is a bare minimum and 1000 or
	               more is needed for reliable  alpha % confi-
	               dence points, alpha > .95 say. Total number
	               of bootstrap samples is nboott*nbootsd.
	
	           VS: If true, a variance stabilizing transforma-
	               tion is estimated, and the interval is con-
	               structed on the transformed scale, and then
	               is mapped back to the original theta scale.
	               This can improve both the statistical proper-
	               ties of the intervals and speed up the compu-
	               tation. See the reference Tibshirani (1988)
	               given below.  If false,  variance stabiliza-
	               tion is not performed.
	
	     v.nbootg: The number of bootstrap samples used to esti-
	               mate the variance stabilizing transformation
	               g.  Only used if VS=T.
	
	    v.nbootsd: The number of bootstrap samples used to esti-
	               mate the standard deviation of theta(x).
	               Only used if VS=T.
	
	     v.nboott: The number of bootstrap samples used to esti-
	               mate the distribution of the bootstrap T
	               statistic. Only used if VS=T. Total number of
	               bootstrap samples is v.nbootg*v.nbootsd +
	               v.nboott
	
	         perc: Confidence points desired.
	
	_V_a_l_u_e_s:
	
	     list with the following components:
	
	confpoints: Estimated confidence points
	
	theta, g: theta and g are only returned if VS=T was speci-
	          fied. (theta[i],g[i]),  i=1,length(theta)
	          represents the estimate of the variance stabiliz-
	          ing transformation g at the points theta[i].
	
	_R_e_f_e_r_e_n_c_e_s:
	
	     Tibshirani, R. (1988) "Variance stabilization and the
	     bootstrap". Biometrika (1988) vol 75 no 3 pages 433-44.
	
	     Hall, P. (1988) Theoretical comparison of bootstrap
	     confidence intervals. Ann. Statisi. 16, 1-50.
	
	     Efron, B. and Tibshirani, R. (1993) An Introduction to
	     the Bootstrap.  Chapman and Hall, New York, London.
	
	_E_x_a_m_p_l_e_s:
	
	     #  estimated confidence points for the mean
	     x <- rchisq(20,1)
	     theta <- function(x)mean(x)
	     results <- boott(x,theta)
	     # estimated confidence points for the mean,
	     #  using variance-stabilization bootstrap-T method
	     results <-  boott(x,theta,VS=T)
	     resultsonfpoints          # gives confidence points
	     # plot the estimated var stabilizing transformation
	     plot(resultsheta,results)
	     # use standard formula for stand dev of mean
	     # rather than an inner bootstrap loop
	     sdmean <- function(x)
	         sqrt(var(x)/length(x))
	     results <-  boott(x,theta,sdfun=sdmean)
	
	     # To bootstrap functions of more  complex data structures,
	     # write theta so that its argument x
	     #  is the set of observation numbers
	     #  and simply  pass as data to boot the vector 1,2,..n.
	     # For example, to bootstrap
	     # the correlation coefficient from a set of 15 data pairs:
	     xdata <- matrix(rnorm(30),ncol=2)
	     n <- 15
	     theta <- function(x, xdata) cor(xdata[x,1],xdata[x,2])
	     results <- boott(1:n,theta, xdata)