bayesDensity            package:bayesSurv            R Documentation

_S_u_m_m_a_r_y _f_o_r _t_h_e _d_e_n_s_i_t_y _e_s_t_i_m_a_t_e _b_a_s_e_d _o_n _t_h_e _B_a_y_e_s_i_a_n _m_o_d_e_l.

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

     Compute the conditional (given the number of mixture components)
     and unconditional estimate of the density function based on the
     values sampled using the reversible jumps McMC (McMC average
     evaluated in a grid of values). Give also the values of each
     sampled density evaluated at that grid (returned as the attribute
     of the resulting object). Methods for printing and plotting are
     also provided.

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

     bayesDensity(dir = getwd(), stgrid, grid,
         n.grid = 100, skip = 0, standard = TRUE, unstandard = TRUE)

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

     dir: directory where to search for files (`mixmoment.sim',
          `mweight.sim', mmean.sim', mvariance.sim') with the McMC
          sample.

  stgrid: grid of values at which the sampled standardized densities
          are to be evaluated. If 'missing', the grid is automatically
          computed.

    grid: grid of values at which the sampled densities are to be
          evaluated. If 'missing', the grid is guessed from the first
          20 sampled mixtures as the sequence starting with the minimal
          sampled mixture mean minus 3 standard deviations of the
          appropriate mixture component, ending with the maximal
          sampled mixture mean plus 3 standard deviations of the
          appropriate mixture component, of the length given by
          'n.grid'.

  n.grid: the length of the grid if 'grid = NULL'.

    skip: number of rows that should be skipped at the beginning of
          each file with the stored sample.

standard: if 'TRUE' then also standardized (zero mean, unit variance)
          sampled densities are evaluated.

unstandard: of 'TRUE' then also original (unstandardized) sampled
          densities are evaluated.

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

     An object of class 'bayesDensity' is returned. This object is a
     list and has potentially two components: 'standard' and
     'unstandard'. Each of these two components is a 'data.frame' with
     as many rows as number of grid points at which the density was
     evaluated and with columns called `grid', `unconditional' and `k =
     1', ..., `k = k.max' giving a predictive errr density, either
     averaged over all sampled ks (unconditional) or averaged over a
     psecific number of mixture components.

     Additionally, the object of class 'bayesDensity' has three
     attributes: 

sample.size: a vector of length '1 + kmax' giving the frequency of each
          k in the sample.

 moments: a 'data.frame' with columns called `intercept' and `scale'
          giving the mean and variance of the sampled mixture at each
          iteration of the McMC.

       k: a 'data.frame' with one column called `k' giving number of
          mixture components at each iteration.


     There exists methods to print and plot objects of the class
     'bayesDensity'.

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

     Arnost Komarek arnost.komarek@med.kuleuven.ac.be

