boot.array               package:boot               R Documentation

_B_o_o_t_s_t_r_a_p _R_e_s_a_m_p_l_i_n_g _A_r_r_a_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function takes a bootstrap object calculated by one of the
     functions 'boot', 'censboot', or 'tilt.boot' and returns the
     frequency (or index) array for the bootstrap resamples.

_U_s_a_g_e:

     boot.array(boot.out, indices=)

_A_r_g_u_m_e_n_t_s:

boot.out: An object of class '"boot"' returned by one of the generation
          functions for such an object. 

 indices: A logical argument which specifies whether to return the
          frequency array or the raw index array.  The default is
          'indices=FALSE' unless 'boot.out' was created by 'tsboot' in
          which case the default is 'indices=TRUE'. 

_D_e_t_a_i_l_s:

     The process by which the original index array was generated is
     repeated with the same value of '.Random.seed'.  If the frequency
     array is required then 'freq.array' is called to convert the index
     array to a frequency array.

     A resampling array can only be returned when such a concept makes
     sense.  In particular it cannot be found for any parametric or
     model-based resampling schemes.  Hence for objects generated by
     'censboot' the only resampling scheme for which such an array can
     be found is ordinary case resampling. Similarly if 'boot.out$sim'
     is '"parametric"' in the case of 'boot' or '"model"' in the case
     of 'tsboot' the array cannot be found.  Note also that for
     post-blackened bootstraps from 'tsboot' the indices found will
     relate to those prior to any post-blackening and so will not be
     useful.

     Frequency arrays are used in many post-bootstrap calculations such
     as the jackknife-after-bootstrap and finding importance sampling
     weights. They are also used to find empirical influence values
     through the regression method.

_V_a_l_u_e:

     A matrix with 'boot.out$R' rows and 'n' columns where 'n' is the
     number of observations in 'boot.out$data'.  If 'indices' is
     'FALSE' then this will give the frequency of each of the original
     observations in each bootstrap resample. If 'indices' is 'TRUE' it
     will give the indices of the bootstrap resamples in the order in
     which they would have been passed to the statistic.

_S_i_d_e _E_f_f_e_c_t_s:

     This function temporarily resets '.Random.seed' to the value in
     'boot.out$seed' and then returns it to its original value at the
     end of the function.

_S_e_e _A_l_s_o:

     'boot', 'censboot', 'freq.array', 'tilt.boot', 'tsboot'

_E_x_a_m_p_l_e_s:

     #  A frequency array for a nonparametric bootstrap
     city.boot <- boot(city, corr, R=40, stype="w")
     boot.array(city.boot)

     perm.cor <- function(d,i) 
          cor(d$x,d$u[i])
     city.perm <- boot(city, perm.cor, R=40, sim="permutation")
     boot.array(city.perm, indices=TRUE)