invgauss               package:statmod               R Documentation

_I_n_v_e_r_s_e _G_a_u_s_s_i_a_n _D_i_s_t_r_i_b_u_t_i_o_n

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

     Density, cumulative probability, quantiles and random generation
     for the inverse Gaussian distribution.

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

     dinvgauss(x, mu, lambda=1)
     pinvgauss(q, mu, lambda=1)
     qinvgauss(p, mu, lambda=1)
     rinvgauss(n, mu, lambda=1)

_A_r_g_u_m_e_n_t_s:

       x: vector of quantiles. Missing values (NAs) are allowed.

       q: vector of quantiles. Missing values (NAs) are allowed.

       p: vector of probabilities. Missing values (NAs) are allowed.

       n: sample size. If 'length(n)' is larger than 1, then
          'length(n)' random values are returned.

      mu: vector of (positive) means. This is replicated to be the same
          length as 'p' or 'q' or the number of deviates generated.

  lambda: vector of (positive) precision parameters. This is replicated
          to be the same length as 'p' or 'q' or the number of deviates
          generated.

_D_e_t_a_i_l_s:

     The inverse Gaussian distribution takes values on the positive
     real line. The variance of the distribution is $mu^3/lambda$.
     Applications of the inverse Gaussian include sequential analysis,
     diffusion processes and radiotechniques. The inverse Gaussian is
     one of the response distributions used in generalized linear
     models.

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

     Vector of same length as 'x' or 'q' giving the density
     ('dinvgauss'), probability ('pinvgauss'), quantile ('qinvgauss')
     or random sample ('rinvgauss') for the inverse Gaussian
     distribution with mean 'mu' and inverse dispersion 'lambda'.
     Elements of 'q' or 'p' that are missing will cause the
     corresponding elements of the result to be missing.

_A_u_t_h_o_r(_s):

     Gordon Smyth and Dr Paul Bagshaw, Centre National d'Etudes des
     Telecommunications (DIH/DIPS), France ('qinvgauss')

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

     Chhikara, R. S., and Folks, J. Leroy, (1989). _The inverse
     Gaussian distribution: Theory, methodology, and  applications_.
     Marcel Dekker, New York.

_S_e_e _A_l_s_o:

     'dinvGauss', 'pinvGauss', 'qinvGauss' and 'rinvGauss' in the
     SuppDists package.

_E_x_a_m_p_l_e_s:

     y <- rinvgauss(20,1,2) # generate vector of 20 random numbers
     p <- pinvgauss(y,1,2) # p should be uniform</tt>

