posterior              package:evdbayes              R Documentation

_M_C_M_C _S_a_m_p_l_i_n_g _o_f _P_o_s_t_e_r_i_o_r _D_i_s_t_r_i_b_u_t_i_o_n_s

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

     Constructing MCMC samples of prior and posterior distibutions for
     the location (with optional trend), scale and shape parameters
     under the gev, order statistics or point process models.

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

     posterior(n, init, prior, lh = c("none", "gev", "gpd", "pp","os"), ..., psd,
         burn = 0, thin = 1)

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

       n: The run-length; the number of sampled vectors (excluding
          'init').

    init: Numeric vector of length three/four, giving the initial
          values for the chain, taken to be iteration zero.

   prior: An object of class '"evprior"', constructed using
          'prior.prob', 'prior.quant' or 'prior.norm'.

      lh: A character string specifying the likelihood; either '"gev"'
          for gev, '"gpd"' for gpd, '"os"' for order statistics, '"pp"'
          for Poisson process or '"none"' for none (the default). The
          latter can be used to sample from the prior distribution.

     ...: Arguments to the likelihood. Should include 'data' unless
          'lh' is '"none"'. Should also include 'thresh' and 'noy' if
          'lh' is '"pp"'. Should include the vector 'trend' if a linear
          trend on the location is implemented. See 'pplik' for
          details.

     psd: A vector of length three/four containing standard deviations
          for proposal distributions.

    burn: The burn-in period (an integer); the first 'burn' iterations
          (including 'init') are excluded from the chain.

    thin: The thinning interval (an integer); iteration k is stored
          only if k mod 'thin' is zero (and if k greater than or equal
          to 'burn').

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

     See the user's guide.

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

     A matrix with '1+floor(n/thin)-burn' rows. Row labels give the
     iteration numbers. Column labels give parameter names.

     An attribute 'ar' is also returned. This is a matrix containing
     acceptence rates in the first row (the number of proposals
     accepted divided by the number of iterations) and ``external
     rates'' in the second (the number of proposals that resulted in a
     zero likelihood, divided by the number of iterations).

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

     'pplik', 'prior.prob'

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

     mat <- diag(c(10000, 10000, 100))
     pn <- prior.norm(mean = c(0,0,0), cov = mat)
     ## Not run: data(portpirie)
     ## Not run: 
     posterior(1000, init = c(5,1,0.1), prior = pn, lh = "gev",
         data = portpirie, psd = c(.02,.1,.1))
     ## End(Not run)

