bootpred              package:bootstrap              R Documentation

_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

_D_e_s_c_r_i_p_t_i_o_n:

     See Efron and Tibshirani (1993) for details on this function.

_U_s_a_g_e:

        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 observation.

       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 predictors and fit object
          produced by 'theta.fit'. See example below.

err.meas: function specifying error measure for a single response 'y'
          and prediction 'yhat'. See examples below

     ...: any additional arguments to be passed to 'theta.fit'

_V_a_l_u_e:

     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.

    call: The deparsed call

_R_e_f_e_r_e_n_c_e_s:

     Efron, B. (1983). Estimating the error rate of a prediction 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)%*%fit$coef         
                    }    
        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

