glmm                package:GLMMGibbs                R Documentation

_G_L_M_M_s _B_y _G_i_b_b_s _S_a_m_p_l_i_n_g

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

     `glmm' estimates the posterior distribution of the fixed effects
     of a Generalised Linear Mixed Model.  It also estimates the
     hyperparameter related to each random effect, and the effect
     values of each random effect declared to be ``of interest'' by
     function `Ra'.

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

      glmm(formula, family, data, weights, offset, icm = 50,
           burnin = 1000, keep = 1000, model.show = FALSE,
           progress.info = 0, store.results = FALSE,  thin = 1,
           bugsfile, seed1 = 6762, seed2 = 92928, seed3 = 19729)

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

 formula: The model formula for the model to be fitted.

  family: A description of the error distribution and link function to
          be used. At present the available options are  `"binomial"'
          and `"poisson"'.  The canonical link must always be used, so
          there is no `link' argument. 

    data: The name of a data frame in which the data is stored. (at
          present, the function can only be used with the data in a
          data frame)

 weights: A vector of weights.

     icm: The number of steps of deterministic maximisation of the
          posterior distribution  by the Iterative Conditional Mode
          algorithm before sampling begins.  All hyperparameters are
          fixed to 1.0 . 

  burnin: The number of steps of Gibbs sampling taken before storage of
          the parameter values begins. 

    keep: The number of steps of Gibbs sampling taken once  storage of
          the parameter values has begun. 

    thin: An integer t specifying that one storage of the parameter
          values has begun every (t) th iteration is saved

model.show: A debugging argument used by he developers which will be
          deleted at the final release.

progress.info: If an integer, n say, the functions reports when every n
          iterations have been carried out

bugsfile: A character string.  If not missing, the function creates two
          files, with this string their primary filenames and `.ind'
          and `.out' their secondary filenames  (extensions) .These
          contain the output in {\tt BUGS } format. 

store.results: if `TRUE', returns the sampled values of the parameters
          (see ``Value'' below) 

   seed1: The first of three seeds supplied to the Random Number
          Generator used by the underlying `C' code

   seed2: The second seed.

   seed3: The third seed.

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

     Generalised Linear Mixed Models (GLMMs) are an extension of GLMs
     with the addition of ``random effects''  given whose values the
     response values are conditionally independent. The function `glmm'
     fits these models in a Bayesian paradigm by Gibbs sampling.

     Full details are given in the document "`GLMMGibbs': An R Package
     for Estimating Bayesian Generalised Linear Mixed Models by Gibbs
     Sampling", supplied with this package.

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

     an object  of class `glmmfit', which contains the sample
     statistics of the sampled values and, if the `store.results'
     argument is set to `TRUE', the sampled values themselves.

_N_o_t_e:

     `GLMMgibbs' (the package from which glmm comes) is a beta release
     and we strongly recommend the use of `save.image()' before
     `glmm()' is used

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

     Jonathan Myles, Imperial Cancer Research Fund, and David Clayton,
     Wellcome Trust mylesj@icrf.icnet.uk

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

     Clayton, D.G. (1996) Generalized Linear Mixed Models in Markov
     chain Monte Carlo in Practice, ed. Gilks, W. R. and Richardson, S.
     and  Spiegelhalter, D. J., Chapman & Hall.

