simnewsamples              package:gaga              R Documentation

_P_o_s_t_e_r_i_o_r _p_r_e_d_i_c_t_i_v_e _s_i_m_u_l_a_t_i_o_n

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

     Simulates parameters and data from the posterior and posterior
     predictive distributions, respectively, of a GaGa or MiGaGa model.

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

     simnewsamples(gg.fit, groupsnew, sel, x, groups)

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

  gg.fit: GaGa or MiGaGa fit (object of type 'gagafit', as returned by
          'fitGG'). 

groupsnew: Vector indicating the group that each new sample should
          belong to. 'length(groupsnew)' is the number of new samples
          that will be generated. 

     sel: Numeric vector with the indexes of the genes we want to draw
          new samples for (defaults to all genes). If a logical vector
          is indicated, it is converted to '(1:nrow(x))[sel]'.

       x: 'ExpressionSet', 'exprSet', data frame or matrix containing
          the gene expression measurements used to fit the model.

  groups: If 'x' is of type 'ExpressionSet' or 'exprSet', 'groups'
          should be the name of the column in 'pData(x)' with the
          groups that one wishes to compare. If 'x' is a matrix or a
          data frame, 'groups' should be a vector indicating to which
          group each column in x corresponds to.

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

     The shape parameters are actually drawn from a gamma approximation
     to their posterior distribution. The function 'rcgamma' implements
     this approximation.

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

     Object of class 'ExpressionSet'. Expression values can be accessed
     via 'exprs(object)' and the parameter values used to generate the
     expression values can be accessed via 'fData(object)'.

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

     David Rossell

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

     Rossell D. GaGa: a simple and  flexible hierarchical model for
     microarray data analysis. <URL:
     http://rosselldavid.googlepages.com>.

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

     'checkfit' for posterior predictive plot, 'simGG' for prior
     predictive simulation.

