parest                 package:gaga                 R Documentation

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

     Obtains parameter estimates and posterior probabilities of
     differential expression after a GaGa or MiGaGa model has been fit
     with the function 'fitGG'.

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

     parest(gg.fit, x, groups, burnin, alpha=.05)

_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.

  burnin: Number of MCMC samples to discard. Ignored if 'gg.fit' was
          fit with the option 'method=='EBayes''.

   alpha: If 'gg.fit' was fit with the option 'method=='Bayes'',
          'parest' also computes '1-alpha' posterior credibility
          intervals.

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

     If 'gg.fit' was fit via MCMC posterior sampling (option
     'method=='Bayes''), 'parest' discards the first 'burnin'
     iterations and uses the rest to obtain point estimates and
     credibility intervals for the hyper-parameters. To compute
     posterior probabilities of differential expression the
     hyper-parameters are fixed to their estimated value, i.e. not
     averaged over MCMC iterations.

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

     An object of class 'gagafit', with components: 

 parest : Hyper-parameter estimates.

   mcmc : Object of class 'mcmc' with posterior draws for
          hyper-parameters. Only returned if 'method=='Bayes''.

   lhood: For 'method=='Bayes'' it is the posterior mean of the
          log-likelihood. For 'method=='EBayes'' it is the
          log-likelihood evaluated at the maximum.

  nclust: Number of clusters.

patterns: Object of class 'gagahyp' indicating which hypotheses
          (expression patterns) were tested.

      pp: Matrix with posterior probabilities of differential
          expression for each gene. Genes are in rows and expression
          patterns are in columns (e.g. for 2 hypotheses, 1st column is
          the probability of the null hypothesis and 2nd column for the
          alternative).

_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' to fit a GaGa or MiGaGa model, 'findgenes' to find
     differentially expressed genes and 'posmeansGG' to obtain
     posterior expected expression values. 'classpred' performs class
     prediction.

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

     #Not run
     #library(EBarrays); data(gould)
     #x <- log(exprs(gould)[,-1])  #exclude 1st array
     #groups <- pData(gould)[-1,1]
     #patterns <- rbind(rep(0,3),c(0,0,1),c(0,1,1),0:2) #4 hypothesis
     #gg <- fitGG(x,groups,patterns,method='EBayes')
     #gg
     #gg <- parest(gg,x,groups)
     #gg

