_b_o_o_t_s_t_r_a_p _e_s_t_i_m_a_t_e_s _o_f _p_r_e_d_i_c_t_i_o_n _e_r_r_o_r
	
	     bootpred(x,y,nboot,theta.fit,theta.predict,err.meas,...)
	
	_A_r_g_u_m_e_n_t_s:
	
	            x: a matrix containing the predictor (regressor)
	               values. Each row corresponds to an observa-
	               tion.
	
	            y: a vector containing the response values
	
	        nboot: the number of bootstrap replications
	
	    theta.fit: function to be cross-validated. Takes x and y
	               as an argument.  See example below.
	
	theta.predict: function producing predicted values for
	               theta.fit.  Arguments are a matrix x of pred-
	               ictors and fit object produced by theta.fit.
	               See example below.
	
	     err.meas: function specifying error measure for a sin-
	               gle response y and prediction yhat
	                - see examples below
	
	_V_a_l_u_e_s:
	
	     list with the following components
	
	 app.err: the apparent error rate- that is, the mean value
	          of err.meas when theta.fit is applied to x and y,
	          and then used to predict y.
	
	   optim: the bootstrap estimate of optimism in app.err. A
	          useful estimate of prediction error is
	          app.err+optim
	
	 err.632: the ".632" bootstrap estimate of prediction error.
	
	_R_e_f_e_r_e_n_c_e_s:
	
	     Efron, B. (1983). Estimating the error rate of a pred-
	     iction rule: improvements on cross-validation. J. Amer.
	     Stat. Assoc, vol 78. pages 316-31.
	
	     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:
	
	     # bootstrap prediction error estimation in least squares
	     #  regression
	     x <- rnorm(85)
	     y <- 2*x +.5*rnorm(85)
	     theta.fit <- function(x,y)lsfit(x,y)
	     theta.predict <- function(fit,x)
	                    cbind(1,x)%*%fitoef
	
	     sq.err_function(y,yhat)  (y-yhat)^2
	     results <- bootpred(x,y,20,theta.fit,theta.predict,
	          err.meas=sq.err)
	
	     # for a classification problem, a standard choice
	     # for err.meas would simply count up the
	     #  classification errors:
	     miss.clas <- function(y,yhat) 1*(yhat!=y)
	     # with this specification,  bootpred estimates
	     #  misclassification rate