posmeansGG               package:gaga               R Documentation

_G_e_n_e-_s_p_e_c_i_f_i_c _p_o_s_t_e_r_i_o_r _m_e_a_n_s

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

     Computes posterior means for the gene expression levels using a
     GaGa or MiGaGa model.

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

     posmeansGG(gg.fit, x, groups, sel, underpattern)
     posmeansGG.gagafit(gg.fit, x, groups, sel, underpattern)

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

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

       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.

     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]'.

underpattern: Expression pattern assumed to be true (defaults to last
          pattern in 'gg.fit$patterns'). Posterior means are computed
          under this pattern. For example, if only the null pattern
          that all groups are equal and the full alternative that all
          groups are different are considered, 'underpattern=1' returns
          the posterior means under the assumption that groups are
          different from each other ('underpattern=0' returns the same
          mean for all groups).

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

     The posterior distribution of the mean parameters actually depends
     on the gene-specific shape parameter(s), which is unknown. To
     speed up computations, a gamma approximation to the shape
     parameter posterior is used (see 'rcgamma' for details) and the
     shape parameter is fixed to its mode a posteriori.

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

     Matrix with mean expression values a posteriori, for each selected
     gene and each group. Genes are in rows and groups in columns.

_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:

     'fitGG' for fitting GaGa and MiGaGa models, 'parest' for computing
     posterior probabilities of each expression pattern.

