NMixPredCDFMarg            package:mixAK            R Documentation

_M_a_r_g_i_n_a_l (_u_n_i_v_a_r_i_a_t_e) _p_r_e_d_i_c_t_i_v_e _c_u_m_u_l_a_t_i_v_e _d_i_s_t_r_i_b_u_t_i_o_n _f_u_n_c_t_i_o_n

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

     This function serves as an inference tool for the MCMC output
     obtained using the function 'NMixMCMC'. It computes estimated
     posterior predictive cumulative distribution function for each
     margin.

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

     NMixPredCDFMarg(x, ...)

     ## Default S3 method:
     NMixPredCDFMarg(x, scale, K, w, mu, Li, Krandom=TRUE, ...)

     ## S3 method for class 'NMixMCMC':
     NMixPredCDFMarg(x, grid, lgrid=50, scaled=FALSE, ...)

     ## S3 method for class 'GLMM_MCMC':
     NMixPredCDFMarg(x, grid, lgrid=50, scaled=FALSE, ...)

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

       x: an object of class 'NMixMCMC' for 'NMixPredCDFMarg.NMixMCMC'
          function.

          An object of class 'GLMM_MCMC' for
          'NMixPredCDFMarg.GLMM_MCMC' function.

          A list with the grid values (see below) for
          'NMixPredCDFMarg.default' function. 

   scale: a two component list giving the 'shift' and the 'scale'.

       K: either a number (when 'Krandom'='FALSE') or a numeric vector
          with the chain for the number of mixture components.

       w: a numeric vector with the chain for the mixture weights.

      mu: a numeric vector with the chain for the mixture means.

      Li: a numeric vector with the chain for the mixture inverse
          variances (lower triangles only).

 Krandom: a logical value which indicates whether the number of mixture
          components changes from one iteration to another.

    grid: a numeric vector or a list with the grid values in which the
          predictive CDF should be evaluated.

          If 'x$dim' is 1 then 'grid' may be a numeric vector. If
          'x$dim' is higher than then 'grid' must be a list with
          numeric vectors as components giving the grids for each
          margin.

          If 'grid' is not specified, it is created automatically using
          the information from the posterior summary statistics stored
          in 'x'. 

   lgrid: a length of the grid used to create the 'grid' if that is not
          specified.

  scaled: if 'TRUE', the CDF of shifted and scaled data is summarized.
          The shift and scale vector are taken from the 'scale'
          component of the object 'x'.

     ...: optional additional arguments.

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

     An object of class 'NMixPredCDFMarg' which has the following
     components:   

       x: a list with the grid values for each margin. The components
          of the list are named 'x1', ... or take names from 'grid'
          argument.

   freqK: frequency table for the values of K (numbers of mixture
          components) in the MCMC chain.

   propK: proportions derived from 'freqK'.

MCMC.length: the length of the MCMC used to compute the predictive
          densities.

     cdf: a list with the computed predictive CDF's for each margin.
          The components of the list are named '1', ..., i.e.,
          'cdf[[1]]'='cdf[["1"]]' is the predictive density for margin
          1 etc.

    cdfK: a list with the computed predictive CDF's for each margin,
          conditioned further by K. The components of the list are
          named '1', .... That is, 'cdf[[1]][[1]]' = 'cdf[["1"]][[1]]'
          is the predictive CDF for margin 1 conditioned by K=1,
          'cdf[[1]][[2]]' = 'cdf[["1"]][[2]]' is the predictive CDF for
          margin 1 conditioned by K=2 etc.

          Note that 'cdfK' provides some additional information only
          when 'Krandom' = 'TRUE' or when 'x' results from the
          'NMixMCMC' call to the reversible jump MCMC. 

     There is also a 'plot' method implemented for the resulting
     object.

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

     Arno&#353t Kom&#225rek arnost.komarek[AT]mff.cuni.cz

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

     Kom&#225rek, A. A new R package for Bayesian estimation of
     multivariate normal mixtures allowing for selection  of the number
     of components and interval-censored data. _Computational
     Statistics and Data Analysis_, *53*, 3932-3947.

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

     'plot.NMixPredCDFMarg', 'NMixMCMC', 'GLMM_MCMC'.

