sample                 package:base                 R Documentation

_R_a_n_d_o_m _S_a_m_p_l_e_s _a_n_d _P_e_r_m_u_t_a_t_i_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

     'sample' takes a sample of the specified size from the elements of
     'x' using either with or without replacement.

_U_s_a_g_e:

     sample(x, size, replace = FALSE, prob = NULL)

_A_r_g_u_m_e_n_t_s:

       x: Either a (numeric, complex, character or logical) vector of
          more than one element from which to choose, or a positive
          integer.

    size: non-negative integer giving the number of items to choose.

 replace: Should sampling be with replacement?

    prob: A vector of probability weights for obtaining the elements of
          the vector being sampled.

_D_e_t_a_i_l_s:

     If 'x' has length 1, sampling takes place from '1:x'.  _Note_ that
     this convenience feature may lead to undesired behaviour when 'x'
     is of varying length 'sample(x)'.  See the 'resample()' example
     below.

     By default 'size' is equal to 'length(x)' so that 'sample(x)'
     generates a random permutation of the elements of 'x' (or '1:x').

     The optional 'prob' argument can be used to give a vector of
     weights for obtaining the elements of the vector being sampled.
     They need not sum to one, but they should be nonnegative and not
     all zero.  If 'replace' is true, Walker's alias method (Ripley,
     1987) is used when there are more that 250 reasonably probable
     values: this gives results incompatible with those from R < 2.2.0,
     and there will be a warning the first time this happens in a
     session.

     If 'replace' is false, these probabilities are applied
     sequentially, that is the probability of choosing the next item is
     proportional to the probabilities amongst the remaining items. The
     number of nonzero weights must be at least 'size' in this case.

_R_e_f_e_r_e_n_c_e_s:

     Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S
     Language_. Wadsworth & Brooks/Cole.

     Ripley, B. D. (1987) _Stochastic Simulation_. Wiley.

_E_x_a_m_p_l_e_s:

     x <- 1:12
     # a random permutation
     sample(x)
     # bootstrap sampling -- only if length(x) > 1 !
     sample(x,replace=TRUE)

     # 100 Bernoulli trials
     sample(c(0,1), 100, replace = TRUE)

     ## More careful bootstrapping --  Consider this when using sample()
     ## programmatically (i.e., in your function or simulation)!

     # sample()'s surprise -- example
     x <- 1:10
         sample(x[x >  8]) # length 2
         sample(x[x >  9]) # oops -- length 10!
     try(sample(x[x > 10]))# error!

     ## This is safer:
     resample <- function(x, size, ...)
       if(length(x) <= 1) { if(!missing(size) && size == 0) x[FALSE] else x
       } else sample(x, size, ...)

     resample(x[x >  8])# length 2
     resample(x[x >  9])# length 1
     resample(x[x > 10])# length 0

