Uniform                package:stats                R Documentation

_T_h_e _U_n_i_f_o_r_m _D_i_s_t_r_i_b_u_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     These functions provide information about the uniform distribution
     on the interval from 'min' to 'max'.  'dunif' gives the density,
     'punif' gives the distribution function 'qunif' gives the quantile
     function and 'runif' generates random deviates.

_U_s_a_g_e:

     dunif(x, min=0, max=1, log = FALSE)
     punif(q, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
     qunif(p, min=0, max=1, lower.tail = TRUE, log.p = FALSE)
     runif(n, min=0, max=1)

_A_r_g_u_m_e_n_t_s:

     x,q: vector of quantiles.

       p: vector of probabilities.

       n: number of observations. If 'length(n) > 1', the length is
          taken to be the number required.

 min,max: lower and upper limits of the distribution.  Must be finite.

log, log.p: logical; if TRUE, probabilities p are given as log(p).

lower.tail: logical; if TRUE (default), probabilities are P[X <= x],
          otherwise, P[X > x].

_D_e_t_a_i_l_s:

     If 'min' or 'max' are not specified they assume the default values
     of '0' and '1' respectively.

     The uniform distribution has density

                          f(x) = 1/(max-min)

     for min <= x <= max.

     For the case of u := min == max, the limit case of X == u is
     assumed, although there is no density in that case and 'dunif'
     will return 'NaN' (the error condition).

     'runif' will not generate either of the extreme values unless 'max
     = min' or 'max-min' is small compared to 'min', and in particular
     not for the default arguments.

_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.

_S_e_e _A_l_s_o:

     '.Random.seed' about random number generation, 'rnorm', etc for
     other distributions.

_E_x_a_m_p_l_e_s:

     u <- runif(20)

     ## The following relations always hold :
     punif(u) == u
     dunif(u) == 1

     var(runif(10000))#- ~ = 1/12 = .08333