Ra                 package:GLMMGibbs                 R Documentation

_C_r_e_a_t_e _R_a_n_d_o_m _F_a_c_t_o_r

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

     Create a random factor to be used in the `glmm' function for 
     fitting generalised linear mixed models by Gibbs sampling

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

     Ra(data, shape=0.001, scale=0.001, type="identity",
     contrast="identity", map="none", zlevel=1, of.interest=FALSE)

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

     `Ra' returns an a factor, which when included in the model passed
     to the `glmm' function, is treated as a random effect.

     The effect values have a multivariate normal prior with mean zero
     and a covariance matrix which depends on the `type' of random
     effect and the parameterisation being used.  Its inverse is the
     product of a fixed matrix and a random scalar hyperparameter,
     which has a gamma prior distribution with given shape and scale
     parameters.

     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:

     A value of class `factor', but with additional attributes.

_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 \em
     Ph{Markov chain Monte Carlo in Practice}, ed. Gilks, W. R. and
     Richardson, S. and  and Spiegelhalter, D. J., Chapman & Hall.

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

     `glmm'

