geometric                package:VGAM                R Documentation

_G_e_o_m_e_t_r_i_c _D_i_s_t_r_i_b_u_t_i_o_n

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

     Maximum likelihood estimation for the geometric distribution.

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

     geometric(link = "logit", earg=list(), expected = TRUE)

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

    link: Parameter link function applied to the parameter prob, which
          lies in the unit interval. See 'Links' for more choices.

    earg: List. Extra argument for the link. See 'earg' in 'Links' for
          general information.

expected: Logical.  Fisher scoring is used if 'expected = TRUE', else
          Newton-Raphson.

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

     A random variable Y has a 1-parameter geometric distribution if
     P(Y=y) = prob * (1-prob)^y for y=0,1,2,.... Here, prob is the
     probability of success, and Y is the number of (independent)
     trials that are fails until a success occurs. Thus the response Y
     should be a non-negative integer. The mean of Y is E(Y) =
     (1-prob)/prob and its variance is Var(Y) = (1-prob)/prob^2. The
     geometric distribution is a special case of the  negative binomial
     distribution (see 'negbinomial').

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

     An object of class '"vglmff"' (see 'vglmff-class'). The object is
     used by modelling functions such as 'vglm', and 'vgam'.

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

     T. W. Yee

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

     Evans, M., Hastings, N. and Peacock, B. (2000) _Statistical
     Distributions_, New York: Wiley-Interscience, Third edition.

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

     'negbinomial', 'Geometric', 'betageometric', 'rbetageom'.

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

     x1 = runif(n <- 1000) - 0.5
     x2 = runif(n) - 0.5
     x3 = runif(n) - 0.5
     eta = 0.2 - 0.7 * x1 + 1.9 * x2
     prob = logit(eta, inverse=TRUE)
     y = rgeom(n, prob)
     table(y)
     fit = vglm(y ~ x1 + x2 + x3, geometric, trace=TRUE, crit="coef")
     coef(fit)
     coef(fit, mat=TRUE)
     summary(fit)

