prior                package:evdbayes                R Documentation

_C_o_n_s_t_r_u_c_t_i_o_n _o_f _P_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 prior distibutions for the location, scale and shape
     parameters using normal, beta or gamma distributions. A linear
     trend for the location can also be specified, using a prior normal
     distribution centered at zero for the trend parameter.

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

     prior.prob(quant, alpha, trendsd = 0)
     prior.quant(prob = 10^-(1:3), shape, scale, trendsd = 0)
     prior.norm(mean, cov, trendsd = 0)

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

quant, alpha: Numeric vectors of length three and four respectively.
          Beta prior distibutions are placed on probability ratios
          corresponding to the quantiles given in 'quant'.

prob, shape, scale: Numeric vectors of length three. Gamma prior
          distibutions, with parameters 'shape' and 'scale', are placed
          on quantile differences corresponding to the probabilities
          given in 'prob'.

mean, cov: The prior distibution for the location, log(scale) and shape
          is taken to be trivariate normal, with mean 'mean' (a numeric
          vector of length three) and covariance matrix 'cov' (a
          symmetric positive definite three by three matrix).

 trendsd: The standard deviation for the marginal normal prior
          distribution (with mean zero) placed on the linear trend
          parameter for the location. If this is zero (the default) a
          linear trend is not implemented.

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

     See the user's guide.

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

     Returns an object of class '"evprior"', which is essentially just
     a list of the arguments passed.

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

     'posterior', 'pplik'

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

     mat <- diag(c(10000, 10000, 100))
     prior.norm(mean = c(0,0,0), cov = mat, trendsd = 10)
     prior.quant(shape = c(38.9,7.1,47), scale = c(1.5,6.3,2.6))
     prior.prob(quant = c(85,88,95), alpha = c(4,2.5,2.25,0.25))

