checkfit                package:gaga                R Documentation

_C_h_e_c_k _g_o_o_d_n_e_s_s-_o_f-_f_i_t _o_f _G_a_G_a _a_n_d _M_i_G_a_G_a _m_o_d_e_l_s

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

     Produces plots to check fit of GaGa and MiGaGa model. Compares
     observed data with posterior predictive distribution of the model.
     Can also compare posterior distribution of parameters with method
     of moments estimates.

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

     checkfit(gg.fit, x, groups, type='data', logexpr=FALSE, xlab, ylab, main, lty, lwd, ...)

_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.

    type: 'data' checks marginal density of the data; 'shape' checks
          shape parameter; 'mean' checks mean parameter; 'shapemean'
          checks the joint of shape and mean parameters

 logexpr: If set to 'TRUE', the expression values are in log2 scale.

    xlab: Passed on to 'plot'

    ylab: Passed on to 'plot'

    main: Passed on to 'plot'

     lty: Ignored.

     lwd: Ignored.

     ...: Other arguments to be passed to 'plot' 

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

     The routine generates random draws from the posterior and
     posterior predictive distributions, fixing the hyper-parameters at
     their estimated value (posterior mean if model was fit with
     'method=='Bayes'' or maximum likelihood estimate is model was fit
     with 'method=='EBayes'').

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

     Produces a plot.

_N_o_t_e:

     Posterior and posterior predictive checks can lack sensitivity to
     detect model misfit, since they are susceptible to over-fitting.
     An alternative is to perform prior predictive checks by generating
     parameters and data with 'simGG'.

_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:

     'simGG' to simulate samples from the prior-predictive
     distribution, 'simnewsamples' to generate parameters and
     observations from the posterior predictive, which is useful to
     check goodness-of-fit individually a desired gene.

